WO2020171388A2 - Procédé d'identification d'un objet ayant un mouvement anormal dans une image compressée à l'aide d'une trajectoire et d'un motif de vecteur de mouvement - Google Patents

Procédé d'identification d'un objet ayant un mouvement anormal dans une image compressée à l'aide d'une trajectoire et d'un motif de vecteur de mouvement Download PDF

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WO2020171388A2
WO2020171388A2 PCT/KR2020/000731 KR2020000731W WO2020171388A2 WO 2020171388 A2 WO2020171388 A2 WO 2020171388A2 KR 2020000731 W KR2020000731 W KR 2020000731W WO 2020171388 A2 WO2020171388 A2 WO 2020171388A2
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vector
motion
image
motion vector
pattern
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PCT/KR2020/000731
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English (en)
Korean (ko)
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WO2020171388A3 (fr
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박정식
배현성
정승훈
이성진
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이노뎁 주식회사
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
    • H04N19/139—Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Definitions

  • the present invention relates to a technique for effectively identifying an object that exhibits an unusual behavior different from other objects, that is, an abnormal motion object, in compressed images such as H.264 AVC and H.265 HEVC.
  • the present invention recognizes the existence of an object through complex image analysis as in the prior art for a compressed image generated by a CCTV camera and observes their behavior, not identifying an abnormal motion object, but a motion vector obtained by parsing the compressed image.
  • the present invention relates to a technique for extracting a moving object region using and effectively identifying an abnormal motion object based on a motion vector trajectory pattern.
  • the present invention relates to a technique for improving the accuracy of abnormal motion object identification by constructing a training data set with a motion vector trajectory pattern obtained from a compressed image and using a neural network to identify an abnormal motion object.
  • the video control system can not only automatically detect the motion of the object in the CCTV video, but also identify the object representing the unusual motion that is distinct from other objects, that is, the abnormal-motion object.
  • the efficiency can be further improved. Not only can the abnormal motion object be monitored more intensively with more interest, but it is also possible to quickly detect post-mortem evidence from large-scale CCTV video data stored in storage.
  • the CCTV camera device provides a compressed image generated by encoding the captured image according to the image compression technology, and the side using the CCTV image reversely decodes the compressed image according to the corresponding technical standard. Therefore, in order to recognize the existence of multiple objects in a CCTV image to which image compression technology is applied and to identify an abnormal motion object among them, the process of analyzing the image after obtaining the reproduced image, that is, the original image uncompressed, by decoding the compressed image. was needed.
  • a video decoding apparatus includes a parser 11, an entropy decoder 12, an inverse converter 13, a motion vector operator 14, a predictor 15, a deblocking filter ( 16). These hardware modules sequentially process the data of the compressed image to decompress the compressed image and restore the original image data. At this time, the parser 11 parses the motion vector and the coding type for the coding unit of the compressed image.
  • This coding unit is generally an image block such as a macroblock or a subblock.
  • FIG. 2 is a flowchart illustrating a process of identifying an abnormal motion object from a CCTV image in an existing image analysis solution.
  • a compressed image is decoded according to H.264 AVC and H.265 HEVC (S10), and frame images of a reproduced image are downscaled to a small image such as 320x240 (S20).
  • the reason for downscale resizing is to reduce the processing burden in the subsequent image analysis process.
  • the moving object is extracted through image analysis (S30), and the behavior of the moving objects is observed through image content analysis for a series of frame images.
  • the motion object is identified (S40).
  • the method for identifying an abnormal motion object of a compressed image using a trajectory and a pattern of a motion vector obtains a motion vector and a coding type for a plurality of image blocks by parsing the bitstream of the compressed image.
  • the first step A second step of removing motion vectors inconsistent with the progression of a series of frames from among the motion vectors; A third step of generating a vector trajectory in units of image blocks by combining motion vectors connected to each other according to a series of frames; A fourth step of comparing a plurality of vector trajectories generated from the compressed image and setting a mass of image blocks having vector trajectories that match each other as a moving object region for the compressed image; A fifth step of setting at least one of the vector trajectories of the moving object region as a moving object vector pattern; A training dataset for neural network learning by statistically analyzing a number of moving object vector patterns acquired in relation to the compressed image and classifying the occurrence frequency as a normal pattern if it is above a preset upper threshold and classifying it as an abnormal pattern if it is below a preset lower threshold.
  • a sixth step of configuring A seventh step of performing machine learning on the neural network using the training dataset in the neural network learning mode; In the neural network application mode, an eighth step of inputting the moving object vector pattern of the moving object region into the neural network and identifying whether an abnormal motion object in the moving object region is based on a neural network operation result.
  • the second step in the present invention includes removing a motion vector lacking connectivity according to frame progress based on a magnitude component and a direction component of the motion vector from among the motion vectors; Removing a motion vector whose connection length according to frame progression is less than or equal to a preset threshold length from among the motion vectors; It is preferable that the configuration comprises a; step of removing a motion vector lacking directionality between the motion vectors connected according to the progress of the frame from among the motion vectors.
  • the third step in the present invention includes the steps of searching for a video block (hereinafter referred to as “I-picture block”) having an intra-coding coding type in the compressed video; Setting the searched I-picture block as a starting point of a vector trajectory; Searching for a video block having a coding type of predictive coding (hereinafter referred to as a'P-picture block') at a position corresponding to the motion vector of the I-picture block in a subsequent frame; Repeating a process of searching for a next P-picture block at a position corresponding to a motion vector of a previously searched P-picture block while proceeding through a series of subsequent frames until a search fails; It is preferable that the configuration includes: generating a vector trajectory by combining the motion vector of the I-picture block as the starting point and the motion vectors of the plurality of P-picture blocks searched above.
  • the computer program according to the present invention is stored in a medium in order to execute a method for identifying an abnormal motion object of a compressed image using the motion vector trajectory and pattern as described above by being combined with hardware.
  • abnormal motion objects that is, objects that exhibit unusual behavior different from other objects
  • CCTV images can be distinguished and identified from CCTV images through computer software processing, thereby increasing the operational efficiency of the video control system, thereby preventing crime and securing post evidence.
  • an advantage that can be achieved effectively.
  • abnormal motion objects can be identified in compressed images with about 1/10 of the amount of computation compared to the prior art.
  • the number of receiving channels can be increased by approximately 10 times or more.
  • FIG. 1 is a block diagram showing a general configuration of a video decoding apparatus.
  • FIG. 2 is a flow chart showing a process of identifying an abnormal motion object from a CCTV image in the prior art.
  • FIG. 3 is a flow chart showing the entire process of identifying an abnormal motion object from a compressed image according to the present invention.
  • FIG. 4 is a diagram showing an example of removing a noise vector in the present invention.
  • FIG. 5 is a diagram showing an example of a motion vector according to frame progress in the present invention.
  • FIG. 6 is a flow chart showing a process of generating a vector trajectory in the present invention.
  • FIG. 7 is a diagram conceptually showing a vector trajectory for one video block in the present invention.
  • FIG. 8 is a view showing an example of extracting a moving object region based on a motion vector trajectory in the present invention.
  • FIG. 9 is a view showing an example of displaying an image block and a moving object area according to a frame progression in the present invention.
  • FIG. 3 is a flow chart showing the overall process of identifying an abnormal motion object in a compressed image according to the present invention.
  • Such an abnormal motion object identification process may be performed by an image analysis server in a system that handles large-scale compressed images, for example, a CCTV video control system or a CCTV video analysis system.
  • syntax information of a video block is obtained by parsing a bitstream of a compressed video, and a moving object region is extracted by using this. Any one or a combination thereof, such as a macroblock and a subblock, may be used as the video block, and a motion vector and a coding type are preferable as syntax information. As shown in FIG. 8, the obtained moving object region does not accurately reflect the boundary of the moving object existing in the image, but has the advantage of high processing speed and high reliability of object extraction.
  • the present invention differs in concept from the prior art in that it does not recognize an object in an image, but extracts a chunk of an image estimated as a moving object. Since image analysis is not performed, moving objects must be extracted without specific information about the contents of the image, and among them, objects that move normally and objects that move strangely must be identified.
  • the present invention it is possible to extract a moving object region and identify an abnormal motion object without decoding a compressed image.
  • the apparatus or software to which the present invention is applied should not perform an operation of decoding a compressed image, the scope of the present invention is not limited.
  • the compressed image is a compressed image from a single channel.
  • the present invention extracts a moving object region based on a trajectory and pattern of a motion vector and identifies an abnormal motion, so it is difficult to apply it to a compressed image from a plurality of channels. For example, it is pointless to obtain a motion vector trajectory and compare patterns between CCTV images photographed at different locations. In CCTV images taken at the same place, it is meaningful to analyze motion vectors because objects (eg, people, cars) show similar movements.
  • objects eg, people, cars
  • the video decoding apparatus By parsing a compressed image for a certain period of time (eg, 5 minutes), it is possible to obtain a plurality of motion vectors and coding type information in relation to a plurality of image blocks (eg, macroblocks, subblocks) constituting the compressed image.
  • the video decoding apparatus performs syntax analysis and motion vector calculation on the bitstream of the compressed video according to video compression standards (eg, H.264 AVC, H.265 HEVC), through which the compressed video The motion vector and coding type are parsed for the video block of.
  • the size of the coding unit for syntax information in the image block is generally about 64x64 pixels to 4x4 pixels, and may be variously set according to the designer's selection.
  • one frame corresponds to a very short time, for example, 0.03 seconds. Therefore, if it is a video (e.g. CCTV video) of a person, a car, or an animal in the real world, it is difficult for the moving object to appear only in a few frames due to its nature, and a considerable number, especially if it is about an object that can obtain meaningful information from the video. It is expected to appear over the frame of.
  • a video e.g. CCTV video
  • the movement of the frame must have a directionality. For example, a person or car cannot change the direction of movement in increments of 0.03 seconds and maintains the direction of movement for a considerable amount of time. Therefore, motion vector continuity is determined in a series of frames, and those without a constant direction over time are considered as noise and removed.
  • this may be noise data inserted in the image compression process, it exists in the image, such as shaking leaves, shaking of the captured image due to the vibration of CCTV cameras, ghosts that appear for a while, and spots in the image due to diffuse reflection of light. It may be an object that doesn't need to be of much interest.
  • a motion vector lacking connectivity according to frame progression may be removed based on the magnitude and direction components of the motion vector. Since it is a motion vector that is not connected to the front and rear frames, it is clearly a noise.
  • a motion vector whose connection length according to frame progression is less than a preset threshold length may be removed. It is a part that appears and disappears for a very short period of time (eg 0.1 seconds), so it is not worth observation in detail.
  • an object is expressed as a mass of a plurality of image blocks, so each image block constituting the object will exhibit the same motion pattern. Accordingly, when frames of the compressed image progress one by one in response to the passage of time, each part of the image block size in the moving object gradually moves its position on the adjacent frame, and such a movement mode exactly corresponds to the motion vector of the corresponding part.
  • a motion vector is basically information that an image encoder generates and inserts into data of a compressed image according to the movement of an individual object contained in an image as time passes. Therefore, if the time is fixed and one frame is viewed, a number of motion vectors appear cluttered, but as time advances, motion vectors related to the same part of the same object are connected to each other in response to the movement of the object over a series of frames. do.
  • a vector trajectory is created by combining motion vectors that are interconnected while each part of the image block size moves over time.
  • the vector trajectory is a concept formed in accordance with the time flow in units of image blocks, and the concept of generating the vector trajectory in the present invention will be described in detail later with reference to FIGS. 6 and 7.
  • a vector trajectory was obtained in units of image blocks from the compressed image. For example, if there are two objects moving in a CCTV image of about 5 minutes, and each of them is about 12 image blocks and 5 image blocks, a total of 17 vector trajectories are generated in the compressed image.
  • vector trajectories By comparing these vector trajectories, matching vector trajectories are grouped into bundles, and a mass of image blocks related to these vector trajectories is estimated as a single object, and then set as a moving object region. For example, it may be determined that vector trajectories in which a distance between vector trajectories is calculated in each frame and a value obtained by summing them into all frames is within a preset threshold are mutually matched. Alternatively, vector trajectories to be bundled together may be selected using a grouping technique.
  • FIG. 8 is a diagram illustrating an example of extracting a moving object region based on a motion vector trajectory in the present invention.
  • objects in the CCTV image move as frames progress in (a) and (b), and accordingly, motion vectors are generated in units of image blocks in each frame, and these motion vectors are converted over time.
  • multiple vector trajectories are formed in CCTV images. It is estimated that approximately 80 vector trajectories have been derived from the CCTV image shown in FIG. 8.
  • Three moving object regions can be extracted as shown in FIG. 8 by bundling a bunch of image blocks having vector trajectories that match each other with respect to these plurality of vector trajectories. They are grouped into a moving object area because they exist in close proximity continuously over a series of frames of CCTV images and show very similar trajectories.
  • Object ID unique object identification information
  • data processing is performed in units of moving object regions, not in units of individual video blocks.
  • a number of vector trajectories showing very similar trajectories are associated with the moving object area.
  • One or several representative vector trajectories can be selected for each moving object area, and a bundle of multiple vector trajectories related to the moving object area can be selected as a moving object vector. It can also be set as a pattern. Since vector trajectories belonging to one moving object area are very similar, handling all of them only complicates data processing, but is not particularly advantageous. It is preferable to select one vector trajectory for each moving object area as a representative and set it as a moving object vector pattern.
  • Compressed images obtained from a single channel such as CCTV images obtained by fixedly photographing a specific point, contain a number of objects (e.g., people, cars, etc.), and the structure and usage of the place are always constant, so the behavior of these objects is It will appear repeatedly within a few. Therefore, conceptually, after observing the movement of multiple objects and acquiring one or several patterns that are generally represented by multiple objects, the moving object vector pattern matching them is classified as a normal pattern, and unusual movements that do not match them are identified. It is divided into the above pattern.
  • objects e.g., people, cars, etc.
  • a video is generated by a CCTV camera that photographs a road
  • the action of walking at a normal speed along a specific direction (ie, the direction in which the length is formed) in the video is likely to be high, so it is classified as a normal pattern.
  • Actions that run in a direction orthogonal to the direction or abruptly stop in the middle of the road are classified as abnormal patterns because the frequency of occurrence will be low in the entire image.
  • the present invention does not analyze the image, there is no specific information about the content of the image. That is, the screen displayed on the system when determining the normal pattern and the abnormal pattern is not FIG. 8 but FIG. 9. You cannot see the video content at all, you can only know the location of the video block where there is some significant movement and the movement according to their frame progression.
  • a plurality of moving object vector patterns are statistically analyzed and classified into a normal pattern or an abnormal pattern according to their occurrence frequency.
  • a plurality of moving object vector patterns are statistically analyzed, and if the frequency of occurrence is greater than or equal to a preset upper threshold, it is classified as a normal pattern, and if it is less than a preset lower threshold, it is classified as an abnormal pattern.
  • the moving object vector pattern of the normal pattern and the moving object vector pattern of the abnormal pattern are classified as described above to form a training dataset for neural network learning.
  • the gap between the upper threshold and the lower threshold can be set large.
  • the upper threshold can be set to 30% and the lower threshold can be set to 5%.
  • the gap between the upper threshold and the lower threshold may be set relatively small, or the same may be set altogether, in order to widen the range of the abnormal pattern discrimination of the neural network.
  • a training dataset of a neural network can be constructed by processing compressed images without human intervention. Therefore, it is possible to construct a large-scale training dataset from a large amount of compressed images obtained in various shooting environments, for example, compressed images for each year generated by CCTV cameras installed at 10,000 points across the country, and use them to highly learn neural networks in a short time. It is possible to let. Through this, it is possible to obtain 10,000 neural network training results suitable for each of these 10,000 points, and through this, the reliability of the abnormal motion object identification result can be increased.
  • the abnormal motion object is identified using the neural network.
  • the moving object vector pattern of the moving object region acquired in (S500) is input into the neural network, and according to the neural network operation result, the moving object vector pattern is determined whether the moving object vector pattern is a normal pattern or an abnormal pattern. If is outputted, the moving object area is identified as an abnormal motion object.
  • the result of identifying the abnormal motion object may be provided to the video control device.
  • the video control device displays and displays the moving object area of the abnormal motion object on the control screen in a prominent form to the control agent.
  • FIG. 6 is a flowchart illustrating a process of generating a vector trajectory for a compressed image in the present invention
  • FIG. 7 is a diagram conceptually illustrating a vector trajectory for one image block in the present invention.
  • the process of FIG. 6 corresponds to step S300 in FIG. 3.
  • it is a process of generating a vector trajectory in units of image blocks by combining motion vectors that are interconnected according to a series of frames. As each part of an object moves over time, a vector trajectory is created by combining motion vectors that are interconnected.
  • 6 shows a detailed process of generating a vector trajectory, in which coding type information is used to search for a starting point of the vector trajectory. Details will be described later.
  • the vector trajectory seeks to find the flow of motion vectors over time.
  • a new object appears in the image, a picture that does not exist in the previous frame appears newly, so the encoder intra-codes this part, and in subsequent frames, predictive coding is performed sequentially using this intra-coded image block.
  • the phenomenon of performing is commonly seen. Consequently, for a new moving object, an intra-coded video block is followed by a series of predictive-coded video blocks.
  • an intra-coded video block in the compressed video is searched and set as the starting point of the vector trajectory.
  • an intra-coded video block is more often referred to as an'I-picture block'.
  • a predictive-coded video block is more often referred to as a'P-picture block'.
  • a'P-picture block' a coding type of predictive coding
  • the present invention can be implemented in the form of a computer-readable code on a nonvolatile computer-readable recording medium.
  • Various types of storage devices exist as such non-volatile recording media, such as hard disks, SSDs, CD-ROMs, NAS, magnetic tapes, web disks, and cloud disks, and codes are distributed and stored in multiple storage devices connected through a network. It can be implemented and executed.
  • the present invention may be implemented in the form of a computer program stored in a medium in order to execute a specific procedure in combination with hardware.

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Abstract

La présente invention concerne une technique d'identification efficace d'un objet ayant un mouvement anormal, c'est-à-dire un objet qui présente un comportement unique qui est différent d'autres objets généralement dans une image compressée telle que H.264 AVC et H.265 HEVC. En particulier, la présente invention concerne une technique d'extraction d'une région d'objet mobile à l'aide d'un vecteur de mouvement obtenu par l'analyse d'une image compressée et l'identification efficace d'un objet ayant un mouvement anormal sur la base d'un motif de trajectoire de vecteur de mouvement, au lieu d'identifier l'objet ayant un mouvement anormal en reconnaissant l'existence d'objets par l'intermédiaire d'une analyse d'image complexe comme dans l'état de la technique, par exemple, par l'intermédiaire d'une image compressée générée par une caméra de télévision en circuit fermé, et en observant le comportement des objets. En particulier, la présente invention concerne une technique d'amélioration de la précision d'identification d'un objet ayant un mouvement anormal par la construction d'un ensemble de données d'apprentissage comprenant un motif de trajectoire de vecteur de mouvement obtenu à partir d'une image compressée et l'apprentissage d'un réseau neuronal à utiliser pour identifier l'objet ayant un mouvement anormal.
PCT/KR2020/000731 2019-02-18 2020-01-15 Procédé d'identification d'un objet ayant un mouvement anormal dans une image compressée à l'aide d'une trajectoire et d'un motif de vecteur de mouvement WO2020171388A2 (fr)

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