CN110933488A - Video editing method and device - Google Patents
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- CN110933488A CN110933488A CN201811093138.5A CN201811093138A CN110933488A CN 110933488 A CN110933488 A CN 110933488A CN 201811093138 A CN201811093138 A CN 201811093138A CN 110933488 A CN110933488 A CN 110933488A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/44016—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving splicing one content stream with another content stream, e.g. for substituting a video clip
Abstract
The present disclosure relates to a video clipping method and apparatus. The method comprises the following steps: performing object detection on the split-mirror segments, and determining a candidate video frame set in the split-mirror segments, wherein video frames in the same candidate video frame set comprise the same detection object; determining at least one target video frame set according to the feature vectors of the detection objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing a first detection object; cutting the video frames in the at least one target video frame set to obtain cut video frames containing the first detection object; and combining the cut video frames to obtain a target video corresponding to the first detection object. The target video corresponding to the first detection object can be automatically clipped, time and labor are saved, and the video clipping effect can be guaranteed.
Description
Technical Field
The present disclosure relates to the field of videos, and in particular, to a video editing method and apparatus.
Background
In the related art, the video is usually edited manually. The manual mode is time-consuming and labor-consuming, and the clipping effect cannot be guaranteed.
Disclosure of Invention
In view of the above, the present disclosure provides a video editing method and apparatus.
According to an aspect of the present disclosure, there is provided a video clipping method including:
performing object detection on the split-mirror segments, and determining a candidate video frame set in the split-mirror segments, wherein video frames in the same candidate video frame set comprise the same detection object;
determining at least one target video frame set according to the feature vectors of the detection objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing a first detection object;
cutting the video frames in the at least one target video frame set to obtain cut video frames containing the first detection object;
and combining the cut video frames to obtain a target video corresponding to the first detection object.
In a possible implementation manner, the cropping the video frame in the at least one target video frame set to obtain a cropped video frame including the first detection object includes:
determining the size of a cutting frame according to the video frame size of the target video;
determining the position of a cutting frame according to the position of the first detection object in the video frames of the at least one target video frame set;
and according to the size of the cutting frame and the position of the cutting frame, cutting the video frame in the at least one target video frame set to obtain a cut video frame containing the first detection object.
In one possible implementation, determining a position of a crop box according to a position of the first detection object in a video frame of the at least one target video frame set includes:
determining an adjusted position of the first detection object in a first video frame according to the position of the first detection object in the first video frame and the position of the first detection object in a video frame adjacent to the first video frame, wherein the first video frame is a video frame in the target video frame set;
and determining the adjusted position of the first detection object in the first video frame as the position of the cutting frame in the first video frame.
In one possible implementation, performing object detection on a split-lens segment, and determining a set of candidate video frames in the split-lens segment includes:
carrying out object detection on the split-mirror segments, and determining video frames containing detection objects of specified categories in the split-mirror segments;
and determining a candidate video frame set in the split-mirror segment according to the video frames of the same detection object in the designated category in the split-mirror segment.
In a possible implementation manner, determining a candidate video frame set in the split-mirror segment according to a video frame that contains a same detection object of a specified category in the split-mirror segment includes:
in the video frames containing the same detection object of the appointed type in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of the candidate video frame set in the split-mirror segment.
In one possible implementation, the first condition includes one or more of:
the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value;
the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold, wherein the second threshold is larger than the first threshold;
and the area of the detection frame corresponding to the detection object is larger than a third threshold value.
In a possible implementation manner, determining at least one target video frame set according to feature vectors of detection objects in different candidate video frame sets of different split-mirror segments includes:
determining a first feature vector of a first detection object;
determining a second feature vector of the detected object in the first candidate video frame set;
and if the similarity between the second feature vector and the first feature vector is greater than a fourth threshold, determining the first candidate video frame set as a target video frame set.
In a possible implementation manner, determining a first feature vector of a first detection object includes any one of the following:
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the first candidate video frame set of the first detection object;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the last candidate video frame set in which the first detection object is detected;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set in which the first detection object is detected.
In one possible implementation, determining a first feature vector of a first detection object includes:
acquiring an object selection request;
determining the first detection object and a video frame to which the first detection object belongs according to the object selection request;
determining the feature vector of the first detection object in the video frame to which the first detection object belongs as the first feature vector of the first detection object.
In one possible implementation, determining a second feature vector of the detected object in the first set of candidate video frames includes:
and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
According to another aspect of the present disclosure, there is provided a video clipping device including:
the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for carrying out object detection on a split-mirror segment and determining a candidate video frame set in the split-mirror segment, and video frames in the same candidate video frame set comprise the same detection object;
the second determination module is used for determining at least one target video frame set according to the feature vectors of the detection objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing the first detection object;
the cutting module is used for cutting the video frames in the at least one target video frame set to obtain cut video frames containing the first detection object;
and the merging module is used for merging the cut video frames to obtain a target video corresponding to the first detection object.
In one possible implementation, the cropping module includes:
the first determining submodule is used for determining the size of the cutting frame according to the video frame size of the target video;
a second determining submodule, configured to determine a position of the crop box according to a position of the first detection object in a video frame of the at least one target video frame set;
and the cutting submodule is used for cutting the video frame in the at least one target video frame set according to the size of the cutting frame and the position of the cutting frame to obtain the cut video frame containing the first detection object.
In one possible implementation, the second determining sub-module includes:
a first determining unit, configured to determine an adjusted position of the first detection object in a first video frame according to a position of the first detection object in the first video frame and a position of the first detection object in a neighboring video frame of the first video frame, where the first video frame is a video frame in the target video frame set;
and the second determining unit is used for determining the adjusting position of the first detection object in the first video frame as the position of the cutting frame in the first video frame.
In one possible implementation manner, the first determining module includes:
the third determining submodule is used for carrying out object detection on the split mirror segment and determining the video frame containing the detection object of the specified category in the split mirror segment;
and the fourth determining submodule is used for determining a candidate video frame set in the split-mirror segment according to the video frames of the same detection object in the designated category in the split-mirror segment.
In one possible implementation, the fourth determining submodule is configured to:
in the video frames containing the same detection object of the appointed type in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of the candidate video frame set in the split-mirror segment.
In one possible implementation, the first condition includes one or more of:
the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value;
the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold, wherein the second threshold is larger than the first threshold;
and the area of the detection frame corresponding to the detection object is larger than a third threshold value.
In one possible implementation manner, the second determining module includes:
a fifth determining submodule, configured to determine a first feature vector of the first detection object;
a sixth determining submodule, configured to determine a second feature vector of the detected object in the first candidate video frame set;
a seventh determining sub-module, configured to determine the first candidate video frame set as a target video frame set if the similarity between the second feature vector and the first feature vector is greater than a fourth threshold.
In one possible implementation, the fifth determining submodule is configured to:
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the first candidate video frame set of the first detection object;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the last candidate video frame set in which the first detection object is detected;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set in which the first detection object is detected.
In one possible implementation, the fifth determining sub-module includes:
an acquisition unit configured to acquire an object selection request;
a third determining unit, configured to determine, according to the object selection request, the first detection object and a video frame to which the first detection object belongs;
a fourth determining unit, configured to determine a feature vector of the first detection object in a video frame to which the first detection object belongs as a first feature vector of the first detection object.
In one possible implementation, the sixth determining sub-module is configured to:
and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
According to another aspect of the present disclosure, there is provided a video clipping device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the above method.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, object detection is performed on a split-lens segment, a candidate video frame set in the split-lens segment is determined, at least one target video frame set is determined according to feature vectors of detection objects in different candidate video frame sets of different split-lens segments, video frames in the at least one target video frame set are cut to obtain cut video frames containing a first detection object, the cut video frames are combined to obtain a target video corresponding to the first detection object, and therefore, the target video corresponding to the first detection object can be automatically cut, time and labor are saved, and a video cutting effect can be ensured.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 shows a flow diagram of a video clipping method according to an embodiment of the present disclosure.
FIG. 2 shows an exemplary flowchart of video clipping method step S13 according to an embodiment of the present disclosure.
FIG. 3 shows an exemplary flowchart of video clipping method step S132 according to an embodiment of the present disclosure.
FIG. 4 shows an exemplary flowchart of video clipping method step S11 according to an embodiment of the present disclosure.
FIG. 5 shows an exemplary flowchart of video clipping method step S12 according to an embodiment of the present disclosure.
Fig. 6 shows an exemplary flowchart of video clipping method step S121 according to an embodiment of the present disclosure.
FIG. 7 shows a block diagram of a video clipping device according to an embodiment of the present disclosure.
FIG. 8 shows an exemplary block diagram of a video clipping device according to an embodiment of the present disclosure.
FIG. 9 is a block diagram illustrating an apparatus 800 for video clips in accordance with an example embodiment.
FIG. 10 is a block diagram illustrating an apparatus 1900 for video clips in accordance with an example embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
FIG. 1 shows a flow diagram of a video clipping method according to an embodiment of the present disclosure. The execution subject of the video clipping method may be a video clipping device. For example, the video clipping method may be performed by a terminal device or a server or other processing device. The terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, or a wearable device. In some possible implementations, the video clipping method may be implemented by a processor calling computer readable instructions stored in a memory. As shown in fig. 1, the method includes steps S11 through S14.
In step S11, object detection is performed on the split-lens segments, and a set of candidate video frames in the split-lens segments is determined, where the video frames in the same set of candidate video frames contain the same detection object.
The object in the embodiments of the present disclosure may be any object such as a human face, a human body, or an object (e.g., a vehicle), and is not limited herein.
In a possible implementation manner, the objective detection may be performed on the split-lens segments by using YOLO (Only one eye is aimed), so as to obtain an objective detection result of the split-lens segments.
It should be noted that, although the manner of performing object detection on the lens segments is described above by taking YOLO as an example, those skilled in the art can understand that the disclosure should not be limited thereto. The person skilled in the art can flexibly select the way of performing object detection on the lens segments according to the actual application scene requirements and/or personal preferences.
In the embodiment of the present disclosure, after the object detection is performed on the lens segments to obtain the object detection result, the video frames containing the same detection object in the object detection result may be combined into the candidate video frame set, so as to obtain at least one candidate video frame set. In the embodiment of the present disclosure, in a case where a certain video frame includes different detection objects, the video frame may belong to a plurality of candidate video frame sets.
In one possible implementation manner, after the object detection result of the split-mirror segment is obtained, the object detection result of the specified category may be filtered according to the category name in the object detection result. In this implementation manner, video frames including the same detection object in the object detection result of the specified category may be grouped into a candidate video frame set, thereby obtaining at least one candidate video frame set.
In one possible implementation, prior to performing object detection on the lens segment, the method further comprises: and carrying out shot segmentation on the first video to obtain at least one lens segment of the first video. In this implementation, the first video represents a video that requires a video clip. The "first" of the first video is merely for convenience of expression and reference herein, and does not imply that there must be a corresponding first video in a particular implementation of the present disclosure. In this implementation, a related art approach may be adopted to perform shot segmentation on the first video. The number of the split-mirror segments of the first video may be one or more.
In step S12, at least one target video frame set is determined according to the feature vectors of the detected objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing the first detected object.
In the embodiment of the present disclosure, image regions where detection objects in different candidate video frame sets are located may be mapped to the same feature space, so that the similarity between feature vectors corresponding to the same detection object in different image regions is higher, and the similarity between feature vectors corresponding to different detection objects is lower. The image area where the detection object is located may represent an area occupied by the detection frame corresponding to the detection object.
In a possible implementation manner, feature vectors of detection objects in candidate video frame sets with non-coincident time may be compared without comparing feature vectors of detection objects in candidate video frame sets with coincident time, so that efficiency of video clipping can be further improved.
In the embodiment of the present disclosure, the first detection object represents a certain detection object. The term "first" in the first detection object is used herein for convenience of expression and reference, and does not mean that there must be a first detection object corresponding thereto in a specific implementation of the present disclosure.
In step S13, a video frame in at least one target video frame set is clipped to obtain a clipped video frame including a first detection object.
In the embodiment of the present disclosure, by clipping, a video frame of a horizontal version video may be clipped to a video frame of a vertical version video, a video frame of a vertical version video may also be clipped to a video frame of a horizontal version video, and a video frame of a video may also be clipped to a video frame meeting other clipping requirements, which is not limited herein.
In one possible implementation, the number of target video frame sets is plural.
In one possible implementation, each video frame in the at least one target video frame set may be cropped separately.
In step S14, the clipped video frames are combined to obtain a target video corresponding to the first detection object.
In the embodiment of the present disclosure, the clipped video frames may be merged according to the sequence of the time of the video frame before clipping in the first video from first to last, so as to obtain the target video corresponding to the first detection object. The audio of the target video can be obtained by combining the audio corresponding to each video frame.
In the embodiment of the disclosure, object detection is performed on a split-lens segment, a candidate video frame set in the split-lens segment is determined, at least one target video frame set is determined according to feature vectors of detection objects in different candidate video frame sets of different split-lens segments, video frames in the at least one target video frame set are cut to obtain cut video frames containing a first detection object, the cut video frames are combined to obtain a target video corresponding to the first detection object, and therefore, the target video corresponding to the first detection object can be automatically cut, time and labor are saved, and a video cutting effect can be ensured.
FIG. 2 shows an exemplary flowchart of video clipping method step S13 according to an embodiment of the present disclosure. As shown in fig. 2, step S13 may include steps S131 to S133.
In step S131, the size of the crop box is determined according to the video frame size of the target video.
In one possible implementation, the video frame size of the target video may be determined as the size of the crop box.
In another possible implementation, the size of the crop box may be slightly smaller than the video frame size of the target video.
In step S132, the position of the crop box is determined according to the position of the first detection object in the video frames of the at least one target video frame set.
In one possible implementation, the position of the crop box in the first video frame may be determined according to the position of the first detection object in the first video frame. For example, the geometric center of the detection frame of the first detection object in the first video frame may be used as the geometric center of the cropping frame in the first video frame.
In step S133, according to the size of the cropping frame and the position of the cropping frame, the video frame in the at least one target video frame set is cropped, so as to obtain a cropped video frame including the first detection object.
In embodiments of the present disclosure, the position of the crop box may be different in different video frames of the set of target video frames. That is, the position of the crop box may move with the first detection object.
FIG. 3 shows an exemplary flowchart of video clipping method step S132 according to an embodiment of the present disclosure. As shown in fig. 3, step S132 may include step S1321 and step S1322.
In step S1321, an adjusted position of the first detection object in the first video frame is determined according to the position of the first detection object in the first video frame and the position of the first detection object in the neighboring video frame of the first video frame, where the first video frame is a video frame in the target video frame set.
In one possible implementation manner, the geometric center of the cropping frame in the first video frame may be determined according to the geometric center of the detection frame of the first detection object in the first video frame and the adjacent video frame of the first video frame.
As one example of this implementation, the neighboring video frames of the first video frame may include N video frames before the first video frame and N video frames after the first video frame. For example, N equals 2, then the neighboring video frames of the first video frame may include 2 video frames before the first video frame and 2 video frames after the first video frame. Wherein, the 2 video frames before the first video frame represent the 2 video frames before and closest to the first video frame; the 2 video frames following the first video frame represent the 2 video frames following and closest to the first video frame.
As an example of this implementation, an average value of geometric centers of the detection frames of the first detection object in the first video frame and the neighboring video frames of the first video frame may be determined as a geometric center of the crop frame in the first video frame.
As another example of this implementation, equation 1 may be employed to determine the geometric center D (x) of a crop box in a first video frameD,yD)：
Wherein, when i ≠ 0, d (x)i,yi) Coordinates representing the geometric center of the detection box in the adjacent video frame i of the first video frame; d (x)0,y0) Coordinates representing a geometric center of the detection box in the first video frame; 2N represents the number of adjacent video frames of the first video frame; omegaiAnd representing the weight corresponding to the coordinate of the geometric center of the detection frame in the adjacent video frame i of the first video frame.
In step S1322, the adjusted position of the first detection object in the first video frame is determined as the position of the crop box in the first video frame.
The embodiment of the disclosure determines the adjustment position of the first detection object in the first video frame according to the position of the first detection object in the first video frame and the position of the first detection object in the adjacent video frame of the first video frame, and determines the adjustment position of the first detection object in the first video frame as the position of the cropping frame in the first video frame, thereby being capable of playing the effect of track smoothing and reducing the jitter of the first detection object in the target video.
FIG. 4 shows an exemplary flowchart of video clipping method step S11 according to an embodiment of the present disclosure. As shown in fig. 4, step S11 may include step S111 and step S112.
In step S111, object detection is performed on the split-lens segment, and a video frame containing a detection object of a specified category in the split-lens segment is determined.
In one possible implementation, performing object detection on the split-lens segment, and determining a video frame containing a detection object of a specified category in the split-lens segment includes: carrying out object detection on the sub-mirror segments, and determining video frames containing detection objects in the sub-mirror segments; and determining the video frames containing the detection objects of the specified category in the split-mirror segment from the video frames containing the detection objects in the split-mirror segment.
In another possible implementation manner, performing object detection on the split-lens segment, and determining a video frame containing a detection object of a specified category in the split-lens segment includes: and detecting the detection object of the specified category in the split-mirror segment, and determining the video frame containing the detection object of the specified category in the split-mirror segment.
In step S112, a candidate video frame set in the split-mirror segment is determined according to the video frames in the split-mirror segment that contain the same detection object of the specified category.
In a possible implementation manner, determining a candidate video frame set in a split-mirror segment according to a video frame containing a same detection object of a specified category in the split-mirror segment includes: in the video frames containing the same detection object of the appointed category in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of a candidate video frame set in the split-mirror segment.
As one example of this implementation, the first condition includes one or more of: the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value; the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold value, wherein the second threshold value is larger than the first threshold value; the area of the detection frame corresponding to the detection object is larger than the third threshold value.
In the implementation mode, by setting the first condition, the video frame of which the detection frame corresponding to the detection object meets the first condition is determined as the candidate video frame, and the video frame of which the detection frame corresponding to the detection object does not meet the first condition is filtered, so that the video frame with obvious detection error can be filtered, the quality of the detection object in the candidate video frame is ensured to be high, and the effect of the subsequent video clip can be ensured.
FIG. 5 shows an exemplary flowchart of video clipping method step S12 according to an embodiment of the present disclosure. As shown in fig. 5, step S12 may include steps S121 to S123.
In step S121, a first feature vector of the first detection object is determined.
In one possible implementation, determining a first feature vector of a first detection object includes: and determining a first feature vector of the first detection object according to the feature vector of the first detection object in the first candidate video frame set of the first detection object. In this implementation, an average value of feature vectors of a first detection object in a first candidate video frame set in which the first detection object is detected may be determined as a first feature vector of the first detection object.
In another possible implementation, determining a first feature vector of a first detection object includes: and determining a first feature vector of the first detection object according to the feature vector of the first detection object in the last candidate video frame set in which the first detection object is detected. In this implementation, an average value of feature vectors of the first detection object in the last candidate video frame set in which the first detection object was detected may be determined as the first feature vector of the first detection object.
In another possible implementation, determining a first feature vector of a first detection object includes: and determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set of which the first detection object is detected. In this implementation, an average value of feature vectors of the first detection object in each candidate video frame set in which the first detection object has been detected may be determined as the first feature vector of the first detection object.
In step S122, a second feature vector of the detected object in the first set of candidate video frames is determined.
In one possible implementation, determining a second feature vector of the detected object in the first set of candidate video frames includes: and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
The first candidate video frame set may represent a candidate video frame set to be compared with the first feature vector of the first detection object.
In step S123, if the similarity between the second feature vector and the first feature vector is greater than the fourth threshold, the first candidate video frame set is determined as the target video frame set.
In this embodiment of the disclosure, if the similarity between the second feature vector and the first feature vector is greater than the fourth threshold, it may be indicated that the detected object in the first candidate video frame set is also the first detected object.
Fig. 6 shows an exemplary flowchart of video clipping method step S121 according to an embodiment of the present disclosure. As shown in fig. 6, step S121 may include steps S1211 to S1213.
In step S1211, an object selection request is acquired.
In step S1212, according to the object selection request, the first detection object and the video frame to which the first detection object belongs are determined.
In step S1213, the feature vector of the first detection object in the video frame to which the first detection object belongs is determined as the first feature vector of the first detection object.
The first detection object and the video frame to which the first detection object belongs are determined according to the object selection request by acquiring the object selection request, and the feature vector of the first detection object in the video frame to which the first detection object belongs is determined as the first feature vector of the first detection object, so that the first detection object and the first feature vector of the first detection object can be determined according to user selection.
FIG. 7 shows a block diagram of a video clipping device according to an embodiment of the present disclosure. As shown in fig. 7, the apparatus includes: a first determining module 71, configured to perform object detection on the split-mirror segments, and determine a candidate video frame set in the split-mirror segments, where video frames in the same candidate video frame set include the same detected object; a second determining module 72, configured to determine at least one target video frame set according to feature vectors of detection objects in different candidate video frame sets of different split-mirror segments, where the target video frame set represents a candidate video frame set including the first detection object; the cropping module 73 is configured to crop video frames in at least one target video frame set to obtain cropped video frames including a first detection object; and a merging module 74, configured to merge the clipped video frames to obtain a target video corresponding to the first detection object.
FIG. 8 shows an exemplary block diagram of a video clipping device according to an embodiment of the present disclosure. As shown in fig. 8:
in one possible implementation, the cropping module 73 includes: a first determining sub-module 731, configured to determine the size of the crop box according to the video frame size of the target video; a second determining sub-module 732, configured to determine a position of the crop box according to a position of the first detected object in a video frame of the at least one target video frame set; the cropping sub-module 733 is configured to crop a video frame in the at least one target video frame set according to the size of the cropping frame and the position of the cropping frame, so as to obtain a cropped video frame including the first detection object.
In one possible implementation, the second determining submodule 732 includes: a first determining unit, configured to determine, according to a position of a first detection object in a first video frame and a position of the first detection object in a neighboring video frame of the first video frame, an adjusted position of the first detection object in the first video frame, where the first video frame is a video frame in a target video frame set; and the second determining unit is used for determining the adjusting position of the first detection object in the first video frame as the position of the cutting frame in the first video frame.
In one possible implementation, the first determining module 71 includes: the third determining submodule 711 is configured to perform object detection on the split-mirror segments, and determine that the split-mirror segments include video frames of detection objects of a specified category; the fourth determining sub-module 712 is configured to determine a candidate video frame set in the split-mirror segment according to the video frames in the split-mirror segment that contain the same detection object of the specified category.
In one possible implementation, the fourth determining submodule 712 is configured to: in the video frames containing the same detection object of the appointed category in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of a candidate video frame set in the split-mirror segment.
In one possible implementation, the first condition includes one or more of: the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value; the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold value, wherein the second threshold value is larger than the first threshold value; the area of the detection frame corresponding to the detection object is larger than the third threshold value.
In one possible implementation, the second determining module 72 includes: a fifth determining submodule 721 configured to determine a first feature vector of the first detection object; a sixth determining submodule 722, configured to determine a second feature vector of the detected object in the first set of candidate video frames; a seventh determining sub-module 723, configured to determine the first candidate video frame set as the target video frame set if the similarity between the second feature vector and the first feature vector is greater than a fourth threshold.
In one possible implementation, the fifth determining submodule 721 is used for any of the following: determining a first feature vector of a first detection object according to the feature vector of the first detection object in a first candidate video frame set of the first detection object; determining a first feature vector of a first detection object according to the feature vector of the first detection object in a last candidate video frame set of the first detection object; and determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set of which the first detection object is detected.
In one possible implementation, the fifth determining submodule 721 includes: an acquisition unit configured to acquire an object selection request; a third determining unit, configured to determine, according to the object selection request, the first detection object and the video frame to which the first detection object belongs; and a fourth determining unit, configured to determine a feature vector of the first detection object in the video frame to which the first detection object belongs as the first feature vector of the first detection object.
In one possible implementation, the sixth determining submodule 722 is configured to: and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
In the embodiment of the disclosure, object detection is performed on a split-lens segment, a candidate video frame set in the split-lens segment is determined, at least one target video frame set is determined according to feature vectors of detection objects in different candidate video frame sets of different split-lens segments, video frames in the at least one target video frame set are cut to obtain cut video frames containing a first detection object, the cut video frames are combined to obtain a target video corresponding to the first detection object, and therefore, the target video corresponding to the first detection object can be automatically cut, time and labor are saved, and a video cutting effect can be ensured.
FIG. 9 is a block diagram illustrating an apparatus 800 for video clips in accordance with an example embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 9, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
FIG. 10 is a block diagram illustrating an apparatus 1900 for video clips in accordance with an example embodiment. For example, the apparatus 1900 may be provided as a server. Referring to FIG. 10, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, MacOS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (22)
1. A video clipping method, comprising:
performing object detection on the split-mirror segments, and determining a candidate video frame set in the split-mirror segments, wherein video frames in the same candidate video frame set comprise the same detection object;
determining at least one target video frame set according to the feature vectors of the detection objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing a first detection object;
cutting the video frames in the at least one target video frame set to obtain cut video frames containing the first detection object;
and combining the cut video frames to obtain a target video corresponding to the first detection object.
2. The method of claim 1, wherein cropping the video frame of the at least one target video frame set to obtain a cropped video frame containing the first detected object comprises:
determining the size of a cutting frame according to the video frame size of the target video;
determining the position of a cutting frame according to the position of the first detection object in the video frames of the at least one target video frame set;
and according to the size of the cutting frame and the position of the cutting frame, cutting the video frame in the at least one target video frame set to obtain a cut video frame containing the first detection object.
3. The method of claim 2, wherein determining a position of a crop box based on a position of the first detected object in a video frame of the at least one set of target video frames comprises:
determining an adjusted position of the first detection object in a first video frame according to the position of the first detection object in the first video frame and the position of the first detection object in a video frame adjacent to the first video frame, wherein the first video frame is a video frame in the target video frame set;
and determining the adjusted position of the first detection object in the first video frame as the position of the cutting frame in the first video frame.
4. The method of claim 1, wherein performing object detection on a split-lens segment, determining a set of candidate video frames in the split-lens segment, comprises:
carrying out object detection on the split-mirror segments, and determining video frames containing detection objects of specified categories in the split-mirror segments;
and determining a candidate video frame set in the split-mirror segment according to the video frames of the same detection object in the designated category in the split-mirror segment.
5. The method of claim 4, wherein determining the set of candidate video frames in the segmented mirror segment according to the video frames in the segmented mirror segment containing the same detection object of the specified category comprises:
in the video frames containing the same detection object of the appointed type in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of the candidate video frame set in the split-mirror segment.
6. The method of claim 5, wherein the first condition comprises one or more of:
the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value;
the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold, wherein the second threshold is larger than the first threshold;
and the area of the detection frame corresponding to the detection object is larger than a third threshold value.
7. The method of claim 1, wherein determining at least one target video frame set according to feature vectors of detected objects in different candidate video frame sets of different split-mirror segments comprises:
determining a first feature vector of a first detection object;
determining a second feature vector of the detected object in the first candidate video frame set;
and if the similarity between the second feature vector and the first feature vector is greater than a fourth threshold, determining the first candidate video frame set as a target video frame set.
8. The method of claim 7, wherein determining the first feature vector of the first detected object comprises any one of:
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the first candidate video frame set of the first detection object;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the last candidate video frame set in which the first detection object is detected;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set in which the first detection object is detected.
9. The method of claim 7, wherein determining the first feature vector of the first detected object comprises:
acquiring an object selection request;
determining the first detection object and a video frame to which the first detection object belongs according to the object selection request;
determining the feature vector of the first detection object in the video frame to which the first detection object belongs as the first feature vector of the first detection object.
10. The method according to any one of claims 7 to 9, wherein determining the second feature vector of the detected object in the first set of candidate video frames comprises:
and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
11. A video clipping apparatus, comprising:
the device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for carrying out object detection on a split-mirror segment and determining a candidate video frame set in the split-mirror segment, and video frames in the same candidate video frame set comprise the same detection object;
the second determination module is used for determining at least one target video frame set according to the feature vectors of the detection objects in different candidate video frame sets of different split-mirror segments, wherein the target video frame set represents a candidate video frame set containing the first detection object;
the cutting module is used for cutting the video frames in the at least one target video frame set to obtain cut video frames containing the first detection object;
and the merging module is used for merging the cut video frames to obtain a target video corresponding to the first detection object.
12. The apparatus of claim 11, wherein the cropping module comprises:
the first determining submodule is used for determining the size of the cutting frame according to the video frame size of the target video;
a second determining submodule, configured to determine a position of the crop box according to a position of the first detection object in a video frame of the at least one target video frame set;
and the cutting submodule is used for cutting the video frame in the at least one target video frame set according to the size of the cutting frame and the position of the cutting frame to obtain the cut video frame containing the first detection object.
13. The apparatus of claim 12, wherein the second determining submodule comprises:
a first determining unit, configured to determine an adjusted position of the first detection object in a first video frame according to a position of the first detection object in the first video frame and a position of the first detection object in a neighboring video frame of the first video frame, where the first video frame is a video frame in the target video frame set;
and the second determining unit is used for determining the adjusting position of the first detection object in the first video frame as the position of the cutting frame in the first video frame.
14. The apparatus of claim 11, wherein the first determining module comprises:
the third determining submodule is used for carrying out object detection on the split mirror segment and determining the video frame containing the detection object of the specified category in the split mirror segment;
and the fourth determining submodule is used for determining a candidate video frame set in the split-mirror segment according to the video frames of the same detection object in the designated category in the split-mirror segment.
15. The apparatus of claim 14, wherein the fourth determination submodule is configured to:
in the video frames containing the same detection object of the appointed type in the split-mirror segment, determining the video frames of which the detection frames corresponding to the detection object meet the first condition as candidate video frames, and respectively taking each candidate video frame as an element of the candidate video frame set in the split-mirror segment.
16. The apparatus of claim 15, wherein the first condition comprises one or more of:
the aspect ratio of a detection frame corresponding to the detection object is larger than a first threshold value;
the aspect ratio of a detection frame corresponding to the detection object is smaller than a second threshold, wherein the second threshold is larger than the first threshold;
and the area of the detection frame corresponding to the detection object is larger than a third threshold value.
17. The apparatus of claim 11, wherein the second determining module comprises:
a fifth determining submodule, configured to determine a first feature vector of the first detection object;
a sixth determining submodule, configured to determine a second feature vector of the detected object in the first candidate video frame set;
a seventh determining sub-module, configured to determine the first candidate video frame set as a target video frame set if the similarity between the second feature vector and the first feature vector is greater than a fourth threshold.
18. The apparatus of claim 17, wherein the fifth determination submodule is configured to any one of:
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the first candidate video frame set of the first detection object;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in the last candidate video frame set in which the first detection object is detected;
determining a first feature vector of the first detection object according to the feature vector of the first detection object in each candidate video frame set in which the first detection object is detected.
19. The apparatus of claim 17, wherein the fifth determination submodule comprises:
an acquisition unit configured to acquire an object selection request;
a third determining unit, configured to determine, according to the object selection request, the first detection object and a video frame to which the first detection object belongs;
a fourth determining unit, configured to determine a feature vector of the first detection object in a video frame to which the first detection object belongs as a first feature vector of the first detection object.
20. The apparatus of any one of claims 17 to 19, wherein the sixth determination submodule is configured to:
and determining the average value of the feature vectors of the detected objects in the first candidate video frame set as a second feature vector of the detected objects in the first candidate video frame set.
21. A video review apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 10.
22. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 10.
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