CN112714316A - Regular mark detection and classification identification method based on video code stream - Google Patents
Regular mark detection and classification identification method based on video code stream Download PDFInfo
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Abstract
A rule mark detection and classification identification method based on video code streams. The invention relates to the technical field of mark detection and identification. A method for realizing rule mark detection and classification identification is characterized in that the rule marks are detected and classified and identified through an obtained video analysis video code stream intra-frame prediction coding code stream prediction mode, a pixel residual error distribution condition value, a pixel residual error value, an inter-frame prediction code stream prediction mode, a motion vector and a pixel residual error distribution condition value. The invention avoids the problem of low detection precision due to illumination change and improves the detection precision; the method avoids time-consuming operations such as integer IDCT transformation, inverse quantization, reconstruction, loop filtering and the like in the video decoding process, and is beneficial to real-time detection of the rule marks.
Description
Technical Field
The invention relates to the technical field of mark detection and identification.
Background
The regular signs refer to signs which have regular geometric shapes and are obviously different from the surrounding environment and play roles of warning, reminding, indicating and the like, such as various traffic signs in the traffic field, various signs of hazard sources and the like. The mark detection has wide application fields in actual life, and the effects of mark detection and identification are required to be guaranteed in intelligent traffic, intelligent video monitoring, object tracking and the like. For example, under the background of increasingly frequent traffic jam and accidents, the automatic detection and identification of the traffic signs are important components of an intelligent traffic system, and the automatic detection and identification of the traffic signs can accurately and timely identify the traffic signs and remind drivers of the traffic signs, so that traffic accidents are avoided, and the automatic detection and identification method has great practical significance in the aspect of traffic safety operation. The conventional rule mark detection and identification method mainly comprises a traditional image detection and identification method and a rule mark detection and identification method based on deep learning. The traditional image detection and identification method is easily influenced by the environment, has poor adaptability to the environment and cannot meet the requirement of high-precision real-time identification; the rule mark detection and identification method based on deep learning has high identification precision, and in order to achieve the high identification precision, a neural network adopted by the deep learning has a complex structure, numerous network parameters and high calculation complexity, and is difficult to meet the real-time requirement of a system. The processing objects of the two methods are still images, the actual monitoring data are data after video compression, and the video data need to be completely decoded and then further processed; the two methods only consider the spatial characteristics of the regular marks when the processing object is a still image, and the actual video sequence contains a plurality of the temporal characteristics of the regular marks and cannot be well utilized.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to directly utilize code stream characteristic information in a video code stream does not need to completely decode the video code stream and then carry out rule mark detection and identification by adopting a traditional method, and time-consuming operations such as inverse quantization, integer IDCT conversion, reconstruction, loop filtering and the like in the decoding process are avoided.
The technical scheme adopted by the invention is as follows: a method for detecting and classifying and recognizing rule marks based on video code stream includes obtaining video code stream, analyzing prediction mode of intra-frame prediction coding code stream, distribution condition value and residual value of pixel, prediction mode of inter-frame prediction code stream, motion vector and residual value of pixel, detecting and classifying and recognizing rule marks, and carrying out the following steps
Dividing each frame of image of the obtained video into a plurality of blocks according to 4x4 pixels, wherein each block is defined as a basic block, partially decoding an intra-frame prediction coding code stream of a video code stream, and performing entropy decoding and inverse scanning on the intra-frame prediction code stream in the partial decoding process without performing inverse quantization, integer IDCT (inverse discrete cosine transform), reconstruction and loop filtering to obtain a basic block prediction mode, a pixel residual error distribution condition value and a pixel residual error value;
step two, judging whether the basic block is a rule mark edge part or not according to the basic block prediction mode, the pixel residual error distribution condition value and the pixel residual error value information, if the macro block prediction mode where the basic block is located and the pixel residual error distribution condition value respectively meet the judgment probability, and if the pixel residual error value information meets the judgment threshold value, the basic block is the rule mark edge part, otherwise, the basic block is not the rule mark edge part; if the prediction mode belongs to the edge part of the rule mark, the macro block prediction mode of the basic block mostly adopts a 4x4 prediction mode instead of a 16x16 prediction mode, the CBP value and the DC component and the AC component of the chroma under the pixel residual condition are mostly not 0, the AC component of the chroma is basically not 0, the pixel residual value is larger, and the UV residual value of the chroma is more obvious.
Performing binarization processing on all basic blocks, performing noise processing on the binarized basic blocks by adopting morphological opening operation, and reconstructing information of edge parts of the binarized basic blocks due to the opening operation by using closing operation to obtain a rule mark A;
step four, carrying out partial decoding on the inter-frame prediction coding code stream of the video code stream, wherein the partial decoding process comprises entropy decoding and inverse scanning of the inter-frame prediction code stream without inverse quantization, integer IDCT conversion, reconstruction and loop filtering, and obtaining a basic block prediction mode, a motion vector and a pixel residual distribution condition value;
and step five, judging and correcting the rule mark A according to the prediction mode, the motion vector and the pixel residual distribution condition value, and determining whether the rule mark A is a rule mark.
The judgment and correction of the regular mark A means that the shape and the ground color of the regular mark A are compared with the definition of the existing regular mark, and the type of the mark A is judged
And partially decoding the inter-frame prediction coding code stream of the video code stream, wherein the decoding process comprises entropy decoding and inverse scanning of the inter-frame prediction code stream to obtain a macro block prediction mode, a motion vector and a pixel residual distribution condition value of a basic block. And judging and correcting the rule mark A according to the prediction mode, the motion vector and the pixel residual distribution condition value, wherein the rule mark A mostly adopts a 16x16 prediction mode as a basic block prediction mode, the motion vector change is small, most of the pixel residual CBP values are zero, the statistical rule mark A comprises the number of the basic blocks adopting the 16x16 prediction modes, the sum value of absolute values of motion vector residuals and the number of the pixel residual CBP values are zero, respectively setting judgment threshold values, and if the judgment threshold values are met, the mark A is a rule mark, otherwise, correcting, and the mark is a pseudo-rule traffic mark.
And judging the basic shape of the regular mark A according to the geometric characteristics of the regular mark, wherein the regular mark is mostly in a standard geometric shape such as a circle, a triangle, a rectangle and the like, and the regular mark can be judged by adopting the geometric attributes such as circularity, rectangularity and elongation to obtain the basic shape of the mark A. The regular mark has a bright base color, the colors such as blue and red which are obviously different from the surrounding colors are mostly adopted, the chroma residual values of the edge coding blocks of the regular mark are obviously different correspondingly to the code stream, and the base color of the regular mark A can be judged by analyzing the chroma residual value condition. The regular marks of different types are different in shape and background color, the type of the regular marks A can be judged according to the shape and the background color of the regular marks A, if the traffic mark indicating marks are circular and blue in background color, and when the judging marks are circular and the background color is blue, the marks can be judged to be the traffic indicating marks, so that a foundation is laid for further identification of the marks.
The invention has the beneficial effects that: the method comprises the steps of obtaining a coding block prediction mode, a pixel residual error distribution condition value and a pixel residual error by partially decoding an intra-frame prediction code stream of a video code stream, detecting a rule mark by analyzing the coding block prediction mode, the pixel residual error distribution condition value and the pixel residual error information, and providing effective information for further identifying the rule mark; the method comprises the steps of obtaining a coding block prediction mode, a pixel residual error distribution condition value and a motion vector by partially decoding an inter-frame prediction code stream of a video code stream, and correcting the regular mark detection by analyzing the coding block prediction mode, the pixel residual error distribution condition value and the motion vector information, so that the detection accuracy is improved; the method comprises the steps that the characteristic that the ground colors of different types of rules mark colors are obviously different is utilized, the characteristic that the chroma residual error of an intra-frame prediction coding block in a video code stream has obviously different characteristics is reflected, and the different types of rule marks are identified by utilizing the pixel residual error information of the coding block; the method utilizes the correlation among the pixels of the coding blocks and the residual error information of the coding blocks to carry out detection, thereby avoiding the problem of low detection precision caused by illumination change and improving the detection precision; the method avoids time-consuming operations such as integer IDCT transformation, inverse quantization, reconstruction, loop filtering and the like in the video decoding process, and is beneficial to real-time detection of the rule marks.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is explained in detail by combining the following embodiments: the invention fully utilizes the code stream information of intra-frame prediction code stream prediction mode, pixel residual distribution condition CBP and pixel residual, inter-frame prediction code stream prediction mode, pixel residual distribution condition CBP and motion vector, and the like in video code stream, and carries out regular mark detection and classification identification based on blocks rather than pixels. The following description will take traffic sign detection and classification as an example.
Assume that the traffic video stream adopts the video compression standard as h.264, and the basic block size is 4x 4. The specific detection and classification identification steps are as follows:
the first step is as follows: and partially decoding the intra-frame prediction code stream of the video code stream to obtain the macro block type, the intra-frame prediction mode, the quantization parameter QP, the pixel residual distribution CBP value and the pixel residual value after partial decoding, wherein time-consuming operations such as integer IDCT transformation, inverse quantization, reconstruction, loop filtering and the like in the decoding process are not required to be completed.
The second step is that: judging the probability P1 that the basic block is the traffic sign edge according to the macro block type and the intra-frame prediction mode, and setting P1 to 0 when the intra-frame prediction mode is the intra-frame 16x16 prediction mode; when the intra prediction mode is the intra 4x4 prediction mode, then P1 is set to 1.
The third step: and judging the probability P2 that the basic block is the edge of the traffic sign according to the CBP value of the residual distribution condition of the pixels of the basic block. The basic block pixel residual distribution CBP value contains whether a luminance DC coefficient, a luminance AC coefficient, a chrominance DC coefficient and a chrominance AC coefficient are 0 or not, and a pixel residual distribution vector a is constructed to be [ a1, a2, a3 and a4 ]]The vector contains 4 elements a1, a2, a3, and a 4. The luminance DC coefficient is 0, a1 is set to 0, otherwise a1 is set to 1; the luminance AC coefficient is 0, setting a2 to 0, otherwise setting a2 to 1; the chroma DC coefficient is 0, setting a3 to 0, otherwise setting a3 to 1; the chroma AC coefficient is 0, setting a 4-0, otherwise setting a 4-1. Setting weight vector w of pixel residual distribution1＝[0.2,0.2,0.3,0.3]Calculating the probability P2-a.w1 T。
The fourth step: and (4) solving the probability P3 to be P1xP2, judging that the basic block is not the edge part of the traffic sign when P3 is less than 0.5, and turning to the seventh step, otherwise, further judging according to the pixel residual value and turning to the fifth step.
The fifth step: taking the absolute value Y of the DC coefficient of brightness as the pixel residual valueDCChroma is taken as the absolute value U of the chroma DC coefficientDCAnd VDCSUM of absolute values of AC coefficients of chromaUACAnd SUMVACSetting threshold values TH according to quantization parametersYDC、THUDC、THVDC、THUACAnd THVAC. Constructing pixel residual value threshold vector b ═ b1, b2, b3, b4, b5]The vector contains 5 elements b1, b2, b3, b4 and b5, where b1 ═ YDC/THYDC,b2＝UDC/THUDC,b3＝VDC/THVDC,b4＝SUMUAC/THUAC,b5＝SUMVAC/THVACSetting a pixel residual value threshold weight vector w2＝[0.1,0.2,0.2,0.25,0.25]Calculating a residual judgment threshold THre＝b.w2 T。
And a sixth step: when TH isre>And when 0.5, judging that the basic block is the edge part of the traffic sign, otherwise, judging that the basic block is not the edge part of the traffic sign.
The seventh step: and (3) carrying out binarization processing on all the basic blocks, wherein the traffic sign is assigned with a value of 255, otherwise, the traffic sign is assigned with a value of 0, carrying out noise processing by adopting morphological opening operation, and reconstructing information of the edge part lost due to the opening operation by using closing operation to obtain the traffic sign A.
Eighth step: the basic shapes of the traffic sign are circular, triangular and rectangular, the shape matching judgment is carried out on the traffic sign A according to the geometric characteristics of the circular, triangular and rectangular shapes, and meanwhile, the edge of the traffic sign A is corrected according to the shape of the traffic sign.
The ninth step: and judging the background color of the traffic sign A according to the residual error threshold condition of the basic blocks at the edge of the traffic sign A. For example, the traffic sign has a blue background color, which is reflected in the UV residual value, and has a large U residual value at the edge of the traffic sign, and a red background color, which is reflected in the UV residual value, and has a large V residual value at the edge of the traffic sign. Suppose that the A edge of the traffic sign comprises n 4x4 basic blocks, and the chroma of each basic block is the absolute value U of the chroma DC coefficientDCiAnd VDCiSUM of absolute values of AC coefficients of chromaUACiAnd SUMVACiThreshold value TH for blue decisionBDCAnd THBACRed decision threshold THRDCAnd THRAC. When U is turnedDCi>THBDCAnd U isACi>THBACJudging that the background color of the traffic sign basic block i is blue when V isDCi>THRDCAnd V isACi>THRACAnd judging that the background color of the traffic sign basic block i is red. Counting n 4x4 basic blocks as blue number m when m>(n-3), the traffic sign has a blue ground color; counting n 4x4 basic blocks as red number m when m>And (n-3), the traffic sign is red in ground color.
The tenth step: and judging which type of traffic sign A is a traffic sign warning sign, a prohibition sign, an indication sign and a road sign according to the shape and the ground color of the traffic sign A.
The eleventh step: and performing partial decoding on the inter-frame prediction code stream of the video code stream to obtain the macro block type, the inter-frame prediction mode, the motion vector residual value and the CBP value of the pixel residual distribution condition after partial decoding, wherein time-consuming operations such as pixel residual decoding, integer IDCT transformation, inverse quantization, reconstruction, loop filtering and the like in the decoding process do not need to be completed.
The twelfth step: and (3) taking the center of the block where the traffic sign A is located as a search starting point, searching 5 whole-pixel motion vectors upwards, downwards, leftwards and rightwards respectively, searching the area which is most matched with the traffic sign A, and taking the sum of absolute values of residual values of the motion vectors as the minimum value to serve as the best matching area B.
The thirteenth step: and counting the probability distribution P of the inter-frame prediction mode of the traffic sign B. When each 4x4 basic block prediction mode is SKIP mode or 16x16 mode, the prediction mode value Mi1, otherwise Mi0. Assuming that the traffic sign B comprises n 4x4 basic blocks, the statistical traffic sign B prediction mode summation value SUMMODE＝∑MiCalculating P as SUMMODAnd/n. When P is present<At 0.7, the traffic sign is corrected, and the traffic sign B is a false traffic sign and is not a traffic sign.
The fourteenth step is that: SUM for summing absolute values of residual values of motion vectors of traffic sign BMVD，
Wherein the content of the first and second substances,
for the 4x4 basic block lateral motion vector residual,
for the 4x4 basic block vertical motion vector residual, ABS is the absolute value operation. When SUMMVD>THMVDTo trafficAnd correcting the mark, wherein the traffic mark B is a fake traffic mark and is not a traffic mark.
The fifteenth step: and according to the pixel residual error distribution CBP value, counting the summation value of 0 of the brightness DC coefficient, the brightness AC coefficient, the chroma DC coefficient and the chroma AC coefficient of the traffic sign B.
Wherein, YDCY represents the case of 4x4 basic block luminance DC coefficient, and when it is not 0DC1, otherwise YDC＝0；YACY represents the case of 4x4 basic block luminance DC coefficient, and when it is not 0AC1, otherwise YAC＝0；UVDCIndicating 4x4 basic block luminance DC coefficient, and when it is not 0, UVDC1, otherwise UVDC＝0；UVACIndicating 4x4 basic block luminance DC coefficient, and when it is not 0, UVAC1, otherwise UVAC0. When SUMCBP>THCBPAnd correcting the traffic sign, wherein the traffic sign B is a fake traffic sign and is not a traffic sign.
While the invention has been described in further detail in connection with specific embodiments thereof, it will be understood that the invention is not limited thereto, and that various other modifications and substitutions may be made by those skilled in the art without departing from the scope of the invention, which is to be determined by the claims appended hereto.
Claims (2)
1. The method for detecting and classifying and identifying the rule marks based on the video code stream is characterized in that: detecting and classifying the rule marks according to the obtained prediction mode, pixel residual distribution condition value and pixel residual value of the intra-frame prediction coding code stream of the video analysis video code stream, the prediction mode, the motion vector and the pixel residual distribution condition value of the inter-frame prediction code stream, and specifically carrying out the following steps
Dividing each frame of image of the obtained video into a plurality of blocks according to 4x4 pixels, wherein each block is defined as a basic block, partially decoding an intra-frame prediction coding code stream of a video code stream, and performing entropy decoding and inverse scanning on the intra-frame prediction code stream in the partial decoding process without performing inverse quantization, integer IDCT (inverse discrete cosine transform), reconstruction and loop filtering to obtain a basic block prediction mode, a pixel residual error distribution condition value and a pixel residual error value;
step two, judging whether the basic block is a rule mark edge part or not according to the basic block prediction mode, the pixel residual error distribution condition value and the pixel residual error value information, if the macro block prediction mode where the basic block is located and the pixel residual error distribution condition value respectively meet the judgment probability, and if the pixel residual error value information meets the judgment threshold value, the basic block is the rule mark edge part, otherwise, the basic block is not the rule mark edge part;
performing binarization processing on all basic blocks, performing noise processing on the binarized basic blocks by adopting morphological opening operation, and reconstructing information of edge parts of the binarized basic blocks due to the opening operation by using closing operation to obtain a rule mark A;
step four, carrying out partial decoding on the inter-frame prediction coding code stream of the video code stream, wherein the partial decoding process comprises entropy decoding and inverse scanning of the inter-frame prediction code stream without inverse quantization, integer IDCT conversion, reconstruction and loop filtering, and obtaining a basic block prediction mode, a motion vector and a pixel residual distribution condition value;
and step five, judging and correcting the rule mark A according to the prediction mode, the motion vector and the pixel residual distribution condition value, and determining whether the rule mark A is a rule mark.
2. The method for detecting and classifying and recognizing rule flags based on video streams as claimed in claim 1, wherein: the judgment and correction of the regular mark A refers to the comparison of the shape and the ground color of the regular mark A with the definition of the existing regular mark, and the judgment of the type of the mark A.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006112642A1 (en) * | 2005-04-19 | 2006-10-26 | Samsung Electronics Co., Ltd. | Method and apparatus for adaptively selecting context model for entropy coding |
| US20140105283A1 (en) * | 2011-06-13 | 2014-04-17 | Jin Li | Method and apparatus for encoding and decoding video using intra prediction mode dependent adaptive quantization matrix |
| EP2840007A1 (en) * | 2013-08-22 | 2015-02-25 | Honda Research Institute Europe GmbH | Consistent behaviour generation of a predictive advanced driver assistant system |
| US20160029021A1 (en) * | 2014-07-22 | 2016-01-28 | Renesas Electronics Corporation | Image receiving device, image transmission system, and image receiving method |
| US20170127090A1 (en) * | 2014-05-29 | 2017-05-04 | Canon Kabushiki Kaisha | Method, apparatus and system for de-blocking a block of video samples |
| CN107404653A (en) * | 2017-05-23 | 2017-11-28 | 南京邮电大学 | A kind of Parking quick determination method of HEVC code streams |
| CN108256511A (en) * | 2018-03-15 | 2018-07-06 | 太原理工大学 | Body movement detection method based on Video coding code stream |
| WO2018147123A1 (en) * | 2017-02-13 | 2018-08-16 | ソニー株式会社 | Image processing device and image processing method |
| CN108596109A (en) * | 2018-04-26 | 2018-09-28 | 济南浪潮高新科技投资发展有限公司 | A kind of object detection method and device based on neural network and motion vector |
| CN109379595A (en) * | 2018-11-15 | 2019-02-22 | 太原理工大学 | Detection dynamic target method based on video code flow Codebook Model |
| WO2020116376A1 (en) * | 2018-12-07 | 2020-06-11 | シャープ株式会社 | Moving image decoding device and moving image encoding device |
| US20200195918A1 (en) * | 2018-12-18 | 2020-06-18 | Renesas Electronics Corporation | Video encoding device, operating methods thereof, and vehicles equipped with a video encoding device |
| CN111741299A (en) * | 2020-07-09 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Method, device and equipment for selecting intra-frame prediction mode and storage medium |
-
2020
- 2020-12-21 CN CN202011522015.6A patent/CN112714316B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006112642A1 (en) * | 2005-04-19 | 2006-10-26 | Samsung Electronics Co., Ltd. | Method and apparatus for adaptively selecting context model for entropy coding |
| US20140105283A1 (en) * | 2011-06-13 | 2014-04-17 | Jin Li | Method and apparatus for encoding and decoding video using intra prediction mode dependent adaptive quantization matrix |
| EP2840007A1 (en) * | 2013-08-22 | 2015-02-25 | Honda Research Institute Europe GmbH | Consistent behaviour generation of a predictive advanced driver assistant system |
| US20170127090A1 (en) * | 2014-05-29 | 2017-05-04 | Canon Kabushiki Kaisha | Method, apparatus and system for de-blocking a block of video samples |
| US20160029021A1 (en) * | 2014-07-22 | 2016-01-28 | Renesas Electronics Corporation | Image receiving device, image transmission system, and image receiving method |
| WO2018147123A1 (en) * | 2017-02-13 | 2018-08-16 | ソニー株式会社 | Image processing device and image processing method |
| CN107404653A (en) * | 2017-05-23 | 2017-11-28 | 南京邮电大学 | A kind of Parking quick determination method of HEVC code streams |
| CN108256511A (en) * | 2018-03-15 | 2018-07-06 | 太原理工大学 | Body movement detection method based on Video coding code stream |
| CN108596109A (en) * | 2018-04-26 | 2018-09-28 | 济南浪潮高新科技投资发展有限公司 | A kind of object detection method and device based on neural network and motion vector |
| CN109379595A (en) * | 2018-11-15 | 2019-02-22 | 太原理工大学 | Detection dynamic target method based on video code flow Codebook Model |
| WO2020116376A1 (en) * | 2018-12-07 | 2020-06-11 | シャープ株式会社 | Moving image decoding device and moving image encoding device |
| US20200195918A1 (en) * | 2018-12-18 | 2020-06-18 | Renesas Electronics Corporation | Video encoding device, operating methods thereof, and vehicles equipped with a video encoding device |
| CN111741299A (en) * | 2020-07-09 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Method, device and equipment for selecting intra-frame prediction mode and storage medium |
Non-Patent Citations (6)
| Title |
|---|
| 刘璐等: "基于全卷积神经网络的国内常见交通警告标志识别", 《长春理工大学学报(自然科学版)》 * |
| 李付江等: "快速AVS帧内预测模式选择算法", 《计算机工程与应用》 * |
| 杨俊等: "智能化交通视频图像处理技术研究", 《电视技术》 * |
| 王俊等: "基于Hi3516的监控视频自适应ROI编码", 《电视技术》 * |
| 苗永利: "基于内容及图像分析的信息技术自动阅卷系统", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 范思宇: "自然场景下的交通标志检测与识别", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
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