CN103049460A - Video surveillance scene information classifying and storing method and search method - Google Patents
Video surveillance scene information classifying and storing method and search method Download PDFInfo
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Abstract
The invention discloses a video surveillance scene information classifying and storing method. Firstly, a face detection or plate number detection technology and other technologies are applied to process surveillance scene video data of a video processing portion, and when face or plate number characteristics are found, a video acquiring device is controlled to snapshot images and record relevant information of the snapshot images. In addition, the snapshot images are stored in real time according to a preset strategy, and accordingly classification and storage of surveillance scene information are achieved. When the method is applied to investigation and evidence collection after an event, stored videos can be quickly indexed by querying the snapshot images and the relevant information, and complete videos of the event can be obtained.
Description
Technical field
The invention belongs to field of video monitoring, be specifically related to a kind of method of video monitoring scene information classification storage.
Background technology
Video monitoring platform is the important component part of safety-protection system, it can be the video data transmitting of monitoring scene to Surveillance center, make the situation of monitored scene very clear, simultaneously can store video data and the voice data of monitored scene, can provide the inquiry foundation for the investigation and evidence collection of certain part event afterwards like this.
The application of current video monitor supervision platform generally is that video capture device is transported to rear end Surveillance center with the video data that gathers, and is stored in the storage server, such as NVR etc., storage in the past mainly relies on the time, the warning classification, regularly, manual etc., realize the video recording storage of multiple situation.Come the playback site of the accident by having access to video recording, this omnidistance playback need to consume a large amount of time, is unfavorable for that the monitor staff adopts an effective measure rapidly afterwards.
In recent years, the application of Intelligent Video Surveillance Technology is more and more extensive.Intelligent Video Surveillance Technology is by Digital Image Processing and analyze the content of understanding in the video pictures, thus the key message in the automatic analysis and drawing out video source.Intelligent video analysis can be from the data of complexity identification behavior and classification, and can continual maintenance operation in 24 hours, effectively control all sound in the monitoring range.Although also far away not as good as video monitoring products such as DVR, NVR, it can effectively reduce monitor staff's labour intensity as a supplementary means of video monitoring to the degree of ripeness of intelligent video analysis product, has promoted quality monitoring at present.
Therefore, take full advantage of Intelligent Video Surveillance Technology, reduce and to have access to time that video recording comes the playback site of the accident to spend and become a an urgent demand in the video surveillance applications instantly.
Summary of the invention
The present invention proposes a kind of method of video monitoring scene information classification storage, purpose is in order to overcome in the traditional video surveillance application, only just according to time, warning classification, regularly or manually store the monitoring scene data, thereby the omnidistance video recording of watching of needs consumes a large amount of time when causing having access to the playing back videos scene afterwards, is unfavorable for adopting an effective measure rapidly.
A kind of method of video monitoring scene information classification storage comprises the steps:
(1) obtains the video data of monitoring scene;
(2) when off-note occurring in the described video data, capture the picture of monitoring scene, and record the relevant information of capturing picture;
The relevant information of described candid photograph image comprises and captures the Characteristic of Image classified information, and the temporal information of capturing image is captured video camera corresponding to image and is connected into the channel number of memory device and captures monitoring place corresponding to image;
(3) picture of candid photograph and the video data classification of monitoring scene are stored.
In the step (2), can use existing intelligent video analysis technical finesse video data, in the video data that obtains continuously, when the behavior of noting abnormalities or feature, begin to capture picture when namely off-note occurring, and record the relevant information of capturing picture.
In the step (2), applied intelligent video analysis technology can be at least a in the detection of people's face, car plate detection, fire detection, the Smoke Detection technology, described people's face detection, car plate detection, fire detection, Smoke Detection technology, be under the prerequisite of moving target appears in monitoring scene, to carry out, namely trigger the monitoring scene feature detection by mobile detection.Can also as required, utilize the detection technique of other Anomalous dynamics videos.
In the step (2), described off-note can be still to occur behind primary detection or the certain hour interval, perhaps captures continuously and arrives.
In the step (2), it is relevant with off-note classification, off-note number, the off-note duration of intelligent video analysis beginning to capture picture, off-note classification, off-note number, off-note duration difference, the mode of capturing picture also is different with frequency.
In the step (3), can be according to default policy class storage during to picture and video data classification storage, default storage policy can be picture-storage with all candid photographs in an independent storage unit, with Video Data Storage in another storage unit.
In the step (3), can trigger the video source storage by mobile detection, perhaps when detecting people's face, car plate, fire, smoke characteristics for the first time, begin to trigger the video source storage, when there is not movable information in monitoring scene, stop stored video data.
In the step (3), described picture-storage with all candid photographs is in an independent storage unit, can further store according to the tagsort information (for example people's face, car plate, fire or smog) of picture, perhaps according to different temporal information classification storages, perhaps according to the classification storage of different monitoring place etc., the retrieval of being more convenient for.
In the step (3), described with the monitoring scene Video Data Storage in an independent storage unit, can be further according to different time point classification storages, perhaps according to different monitoring place classification storages etc., the retrieval of being more convenient for.
The present invention also provides a kind of search method of monitoring scene video data, the picture of the candid photograph that the method acquisition classification that utilizes video monitoring scene information classification of the present invention to store is stored and the video data of monitoring scene, the picture that inquiry is captured, obtain purpose picture and relevant information thereof, the monitoring scene video data that retrieval is corresponding.
Beneficial effect of the present invention is:
1, proposes in the video monitoring, the method of monitoring scene information classification storage, to capture the picture separate, stored in the monitoring scene in a storage unit, when playback video recording or post-survey evidence obtaining, can at first inquire about the candid photograph picture, obtain purpose picture and relevant information thereof, can the quick-searching storage record a video the complete video recording that the acquisition event occurs with it as feature.
2, answer the technology such as the detection of employment face, car plate detection, fire detection, Smoke Detection, start picture and capture, greatly improved and captured the validity of picture, and then reduce memory space and have access to the time that video recording consumes afterwards.
Description of drawings
Fig. 1 is the monitoring scene information classification storage merit block diagram of implementing the inventive method;
Fig. 2 utilizes to capture picture retrieval storage video recording process figure.
Fig. 3 is a kind of monitoring scene information classification of the present invention Stored Procedure figure;
Embodiment
Below in conjunction with accompanying drawing embodiments of the invention are elaborated: present embodiment is realized under take technical solution of the present invention as prerequisite, has provided detailed process, but protection scope of the present invention is not limited to following embodiment.
Present embodiment video monitoring platform Network Based has been realized a kind of method of video monitoring scene information classification storage.Fig. 1 is present embodiment monitoring scene information classification memory function block diagram, video acquisition module can be web camera acquisition monitoring scene video data, then send it to picture and capture module, carry out intelligent video analysis, when noting abnormalities feature or behavior, begin to capture picture, record simultaneously the relevant information of capturing picture, will capture picture and relevant information is stored in the candid photograph picture-storage unit according to default strategy.Video display module can be independent display.
The present embodiment concrete steps comprise:
Step (1), such as Fig. 3, video capture device Real-time Collection monitoring scene video data, and it is sent into the intelligent video analysis part.Require and the video capture device coverage according to monitoring scene, video capture device can be one or more, such as, it can be fixed cameras, also can be monopod video camera, or fixed cameras interlock monopod video camera, or monopod video camera interlock monopod video camera.Such as, when fixed cameras was found moving target, the monopod video camera adjustment movement that can link target was in the monopod video camera field of view center, and regulated zoom and obtain comparatively clearly image, so that subsequent treatment.
Step (2), such as Fig. 3, intelligent video analysis part (utilizing analysis software) is answered respectively the technical finesse monitoring scene video datas such as the detection of employment face, car plate detection, fire detection, Smoke Detection as required.When detecting off-note such as people's face or vehicle license plate characteristic, begin to capture picture, perhaps further according to positional information and the size information of people's face or vehicle license plate characteristic, regulate monopod video camera and point to the feature locations center, regulate zoom, obtain more clearly feature picture.When detecting fire or smoke characteristics, directly begin to capture picture.When in detecting monitoring scene, having for the first time with characteristic information such as face characteristic, begin to capture picture, and the interval short time is captured plurality of pictures, when monitoring scene useful feature information number increases, the interval short time is captured plurality of pictures, and when useful feature information type such as vehicle license plate characteristic increased in the monitoring scene, the interval short time was captured plurality of pictures, when useful feature information number does not increase, no longer capture picture.In the specific implementation, begin to capture picture, it can be that 5 pictures were captured at the interval in 3 seconds that the interval short time is captured plurality of pictures.
When the application of technology in safety monitoring such as the detection of forefathers' face, car plate detection, fire detection and Smoke Detection quite ripe.Such as detecting as example take people's face, people's face detects the process of the position, size and the pose that refer to determine everyone face (if existence) in input picture.Suppose only to exist in the piece image face, then the purpose of facial feature detection is existence and the position of detected characteristics, such as eyes, nose (nostril), eyebrow, mouth (lip), ear etc.Native system can adopt the people's face detection algorithm based on support vector machine.At first adopt method for testing motion, background and moving object are made a distinction, if detect moving object, suppose that so this moving object might be people's face.Then by the model of setting up people's face skin color this moving object is further detected, judge whether this moving object might be human body.If this moving object has the color of human body skin, can determine to have so the zone of people's face skin color, can determine so only just might have in these zones people's face to exist.Next adopt based on people's face detection algorithm of support vector machine and determine whether this zone has people's face, certainly for the simplification problem, therefore the size that can suppose facial image is to change in certain scope, adopts just detecting the zone that may have people's face based on support vector machine classifier and whether having people's face in a certain size scope of core.The facial image of detection system acquisition has just produced like this.Because detecting, people's face of this application system needs to realize real-time detection, and the detection of motion detection and skin color all can realize detecting in real time, if and adopt the core classification device, sorter based on support vector machine, owing to judge just whether very little a part of zone exists people's face, therefore, relatively will process entire image, calculated amount has reduced much also can realize real-time processing.And based on the method for detecting human face of support vector machine because solid theoretical foundation is arranged, neural network has some advantages relatively, the people's face detection scheme that therefore adopts here is feasible.
Step (3) such as Fig. 3, is delivered to video monitoring platform Surveillance center with the picture of candid photograph and the monitoring scene video data transmitting of Real-time Collection.Then according to whether capturing picture image data and associated video data are stored in the different storage unit, perhaps also can when detect off-note such as car plate, just trigger and store the monitoring scene video data.Can greatly reduce like this memory space of video data.
Such as Fig. 1, image data stores into to be captured in the picture-storage unit, and video source stores in the video recording storage unit.The image data of capturing in the picture-storage unit can be according to characteristic type or time point or the classification storage of monitoring place of picture.Video source data in the video recording storage unit also can be according to time point or the classification storage of monitoring place.
By above each step, realized the classification storage of monitoring scene information.Such as Fig. 2, when event replay or post-survey evidence obtaining (when retrieving), can at first inquire about and capture the picture-storage unit, inquire useful picture, obtain this picture and relevant information thereof, with it as characteristic information, can quick-searching the video recording storage unit, the complete video recording that the acquisition event occurs.By at first inquiring about picture, obtain the key word of retrieve stored video recording, can greatly improve the efficient of investigation and evidence collection like this, avoid directly having access under the conventional situation monitoring video and need to consume a large amount of time and manpower, and the problem that can not in time adopt an effective measure.
It is emphasized that; embodiment of the present invention is illustrative; rather than determinate; therefore the present invention is not limited to the embodiment described in the embodiment; every other embodiments that drawn by those skilled in the art's technical scheme according to the present invention belong to the scope of protection of the invention equally.
Claims (10)
1. the method for a video monitoring scene information classification storage is characterized in that, comprises the steps:
(1) obtains the video data of monitoring scene;
(2) when off-note occurring in the described video data, capture the picture of monitoring scene, and record the relevant information of capturing picture;
(3) picture of candid photograph and the video data classification of monitoring scene are stored.
2. the method for video monitoring scene information classification as claimed in claim 1 storage, it is characterized in that, in the step (2), the relevant information of described candid photograph image, comprise and capture the Characteristic of Image classified information, capture the temporal information of image, capture video camera corresponding to image and be connected into the channel number of memory device and capture monitoring place corresponding to image.
3. the method for video monitoring scene information classification storage as claimed in claim 2 is characterized in that, in the step (2), answers at least a processing video data in the detection of employment face, car plate detection, fire detection, the Smoke Detection technology.
4. the method for video monitoring scene information classification as claimed in claim 3 storage, it is characterized in that, in the step (2), described people's face detection, car plate detection, fire detection, Smoke Detection technology are to carry out under the prerequisite of moving target appears in monitoring scene.
5. the method for video monitoring scene information classification storage as claimed in claim 4 is characterized in that, in the step (2), described off-note can be still to occur behind primary detection or the certain hour interval, perhaps captures continuously and arrives.
6. the method that each described video monitoring scene information classification is stored such as claim 1～5, it is characterized in that, in the step (3), during to picture and video data classification storage be picture-storage with all candid photographs in an independent storage unit, with Video Data Storage in another storage unit.
7. the method for video monitoring scene information classification storage as claimed in claim 6 is characterized in that, in the step (3), stops stored video data when there is not movable information in monitoring scene.
8. the method for video monitoring scene information classification as claimed in claim 7 storage, it is characterized in that, when the picture-storage of all candid photographs is in an independent storage unit, tagsort information storage according to picture, perhaps according to different temporal information classification storages, perhaps according to different monitoring place classification storages.
9. the method for video monitoring scene information classification as claimed in claim 8 storage, it is characterized in that, described with the monitoring scene Video Data Storage in an independent storage unit time, according to different time point classification storages, perhaps according to different monitoring place classification storages.
10. the search method of a monitoring scene video data, it is characterized in that, the picture of the candid photograph that the method acquisition classification that utilizes each described video monitoring scene information classification of claim 1～9 to store is stored and the video data of monitoring scene, the picture that inquiry is captured, obtain purpose picture and relevant information thereof, the monitoring scene video data that retrieval is corresponding.
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Application publication date: 20130417 |