Motion Analysis From Encoded Video Bitstream.pdf

MUC LUC VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY NGUYEN MINH HOA MOTION ANALYSIS FROM ENCODED VIDEO BITSTREAM MASTER’S THESIS HA NOI – 2018 VIETNAM NATIONAL UNIVERSI[.]

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY NGUYEN MINH HOA MOTION ANALYSIS FROM ENCODED VIDEO BITSTREAM MASTER’S THESIS HA NOI – 2018 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY NGUYEN MINH HOA MOTION ANALYSIS FROM ENCODED VIDEO BITSTREAM Major: Computer Science MASTER’S THESIS Supervisor: Dr Do Van Nguyen Co-Supervisor: Dr Tran Quoc Long HA NOI - 2018 i AUTHORSHIP “I hereby declare that the work contained in this thesis is of my own and I have not submitted this thesis at any other institution in order to obtain a degree To the best of my knowledge and belief, the thesis contains no materials previously published or written by another person other than those listed in the bibliography and identified as references.” Signature: ……………………………………………… ii SUPERVISOR’S APPROVAL “I hereby approve that the thesis in its current form is ready for committee examination as a requirement for the Master of Computer Science degree at the University of Engineering and Technology.” Signature: ……………………………………………… Signature: ……………………………………………… iii ACKNOWLEDGMENTS First of all, I would like to express special gratitude to my supervisors, Dr Do Van Nguyen and Dr Tran Quoc Long, for their enthusiasm for instructions, the technical explanation as well as advices during this project I also want to give sincere thanks to Assoc Prof Dr Ha Le Thanh, Assoc Prof Dr Nguyen Thi Thuy for the instructions as well as the background knowledge for this thesis And I would like to also thank my teachers, my friends in Human Machine Interaction Lab for their support Thank my friends, my colleagues in the project "Nghiên Cứu Cơng Nghệ Tóm Tắt Video", and project “Multimedia application tools for intangible cultural heritage conservation and promotion”, project number ĐTDL.CN-34/16 for their working and support Last but not least, I want to thank my family and all of my friends for their motivation and support as well They stand by and inspire me whenever I face the tough time 1 TABLE OF CONTENTS AUTHORSHIP i SUPERVISOR’S APPROVAL ii ACKNOWLEDGMENTS iii TABLE OF CONTENTS ABBREVIATIONS List of Figures List of Tables INTRODUCTION CHAPTER LITERATURE REVIEW Moving object detection in the pixel domain Moving object detection in the compressed domain 10 1.2.1 Motion vector approaches 11 1.2.2 Size of Macroblock approaches 13 Chapter Summarization 14 CHAPTER METHODOLOGY 15 Video compression standard h264 15 2.1.1 H264 file structure 15 2.1.2 Macroblock 18 2.1.3 Motion vector 19 Proposed method 21 2.2.1 Process video bitstream 21 2.2.2 Macroblock-based Segmentation 22 2.2.3 Object-based Segmentation 24 2.2.4 Object Refinement 28 Chapter Summarization 28 CHAPTER RESULTS 30 The moving object detection application 30 3.1.1 The process of application 31 3.1.2 The motion information 34 3.1.3 Synthesizing movement information 35 3.1.4 Storing Movement Information 36 Experiments 36 3.2.1 Dataset 36 3.2.2 Evaluation methods 40 3.2.3 Implementations 41 3.2.4 Experimental results 41 Chapter Summarization 44 CONCLUSIONS 45 List of of author’s publications related to thesis 46 REFERENCES 47 ABBREVIATIONS MB Macroblock MV Motion vector NALU Network Abstraction Layer Unit RBSP Raw Byte Sequence Payload SODB String Of Data Bits List of Figures Figure 1.1 The process of moving object detection with data in the pixel domain 10 Figure 1.2 The process of moving object detection with data in the compressed domain 11 Figure 2.1 The structure of a H264 file 15 Figure 2.2 RBSP structure 16 Figure 2.3 Slide structure 18 Figure 2.4 Macroblock structure 18 Figure 2.5 The motion vector of a Macroblock 20 Figure 2.6 The process of moving object detection method 22 Figure 2.7 Skipped Macroblock 23 Figure 2.8 (a) An outdoor and in-door frames (b) The "size-map" of frames, (c) The "motion-map" of frames 24 Figure 2.9 Example about the “consistent” of motion vector 26 Figure 3.1 The implementation process of the approach 33 Figure 3.2 Data struct to storage motion information 35 Figure 3.3 Example frames of test videos 37 Figure 3.4 Example frames and their ground truth 39 Figure 3.5 An example frame of Pedestrians (a) and ground truth image (b) 40 List of Tables Table 2.1 NALU types 16 Table 2.2 Slide types 17 Table 3.1 The information of test videos 38 Table 3.2 The information of test sequences in group 39 Table 3.3 The performance of two approachs with Pedestrians, PETS2006, Highway, and Office 42 Table 3.4 The experimental result of Poppe’s approach on 2nd group 42 Table 3.5 The experimental result of proposed method on 2nd group 43 INTRODUCTION Today, video content is extensively used in the areas of life such as indoor monitoring, traffic monitoring, etc The number of videos sharing over the Internet at any given time is also extremely large According to statistics, hundreds of hours of video are uploaded to Youtube every minute [1] Not only that, the general trend today is the surveillance cameras installed in homes for surveillance and sercurity purposes These cameras will normally operate and store the surveillance videos automatically Only when there are some special situations, or some special events occur, humans will use the video data to revisit The problem is that in a short amount of time, how can such a large video volume be evaluated? For example, when there is a burglary, an intrusion occurs, we can not spend hours to check each video previously stored Then, a tool that lets you determine the moment when an object is moving in a long video is essential to reducing the time and effort of searching Normally, in order to reduce the size of videos for transmission or storing, a video compression procedure is performed at surveillance cameras After that, the compressed information in form of bit stream is stored, or transmitted to a server for analysis The video analysis process needs a lot of features to describe different aspects of vision Typically, these features are extracted from the pixel values of each video frame by fully decompressing bitstream The decompression procedure requires high computation capacity device to perform However, with the trend of "Internet of Things", there are many low processing capacity devices which are not capable for performing this full video decompression at high speed So, it is difficult to perform an approach that requires a lot of computing power in real time Another way to extract the feature from the video is using the data on the compressed video These data can be: transform coefficients, motion vectors, quantization steps, quantization parameters, etc From the above data, through the process and analysis, we can handle some important tasks in the computer vision include moving objects detection, human actions detection, face recognition, motion objects tracking This thesis proposes a new method to determine moving object by exploring and applying some motion estimation techniques in the video compression domain After that, the method will be used to build an application that supports movement searching in the surveillance videos in the families The compression format of the videos in the thesis is the H264 compression standard (MPEG-4 part10), a popular video compression standard today Aims The goal of the thesis is to propose a method for determining moving objects in the compressed domain of a video Then, I try to build an application using the method for support searching the moments which have moving objects in the video Object and Scope of the study Within the framework of the thesis, I study the algorithms related to determining moving objects in video, especially the algorithms that determine moving objects in the compressed domain The video compression standard is used in the thesis is H264/AVC The theory of video compression and computer vision are taken from scientific articles related to the video analysis problem on the compression domain, determine the motion form on the compression domain of the video The videos for test and experiment are obtained from the surveillance cameras both indoor and outdoor Method and procedures - Research on motion analysis and evaluation systems on existing compressed video, scientific articles related to the analysis and evaluation of motion on compressed video - Experimental research: Conduct experiential settings for each theoretical part such as extracting video data, compiling data, and evaluating motion based on the obtained data - Experimental evaluation: Each experiment will be conducted independently on each module and then integrated and deployed Contributions The thesis proposes a new moving object detection method in surveillance video encoded with H264 compression standard using the motion vector and size of macroblock 8 Thesis structure Apart from the introduction, the conclution and the references, this thesis is organized into chapters with the following main contents: Chapter is literature review This chapter will show the related work of the thesis include the moving object detection methods in the pixel domain and the moving object detection methods in the compressed domain Chapter mentiones the basic knowledge about video compression standard H264 such as H264 file structure, macroblocks, motion vectors and describes the detail of moving object detection method including processing video bitstreams, macroblock-based segmentation phase, object-based segmentation phase, and object refinement phase Chapter shows the results of method including an application using proposed method and experimental results 9 CHAPTER LITERATURE REVIEW Today, surveillance cameras are used extensively in the world The volume of video surveillance has also grown tremendously Some problems that are often encountered with video surveillance include event searching, motion tracking, abnormal behavior detection, etc In order to handle these tasks, it is necessary to have a method that can determine which the moments in each videos exist movements Usually, the video is compressed for storage and transmission The previous moving object detection method usually use the data from the pixel images such as color value, edges, etc To get the images that can be displayed, or processed, the system must decode video fully This consumes a large number of computing resources, time and memory of the device I suggest a method that can quickly determine the moving objects in high resolution videos The data used in the method will be taken from the compressed video domain including information about the motion vector and the size of the macroblock (in bit) after encoding The method reduces the processing time of the method considerably compared to methods implemented with data on the pixel domain The problem of motion detection in a video has long been studied This is the first step in a series of computer vision problems such as object tracking, object detection, abnormal movement detection, etc There are usually two approaches to address this problem: using fully decoded video data (pixel domain data) or using live data from an undecoded video (compressed domain data) The following section will outline the studies based on these two approaches Moving object detection in the pixel domain Typically, to reduce the size of the video for transmission, a video encoding process is performed inside the surveillance camera and the compressed information is transmitted as a bit stream to a server for video analysis Common video compression standards used today including mp4, H264, H265 To be viewable, these compressed videos need to be decoded to image frames We call these image frames are the pixel domain and the data obtained from these image frames are the data in the pixel domain Fig 1.1 describes the process of moving object detection methods in the pixel domain The data in the pixel domain include the color values of the pixels, the number of color channels of each pixel, the edges, etc 10 Figure 1.1 The process of moving object detection with data in the pixel domain To determine moving objects in the pixel domain, background subtraction algorithms are commonly used There are many research results that have been introduced long ago These methods usually use data as the relationship between frames in a time series Background subtraction in [2] is defined as: “Background subtraction is a widely used approach for detecting moving objects in videos from static cameras The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called The “background image”, or “background model” As a basic, the background image must be a representation of the scene with no moving objects and must be kept regularly updated so as to adapt to the varying luminarice conditions and geometry settings.” Results of the researchs may include the methods use Gaussian average such as the method of Wren et al [3], the method of Koller et al [4]; the methods use Temporal median filter such as the method of Lo and Velasti [5], the method of Cucchiara et al [6]; the methods using a mixture of Gaussians such as the method of Stauffer and Grimson [7], methods of Wayne Power and Schoonees [8]; etc The above methods have a common characteristic that is the process data are taken by fully decompress the compressed bitstream and this decompression procedure requires a highly computational device to perform However, with the trend of "Internet of Things," where most low-end devices are not capable of performing high-speed decompression Therefore, there should be a video analysis mechanism that includes only uncompressed video Moving object detection in the compressed domain Normally, the videos will be encoded using some compression standard Each compression standard specifies how to shrink the video size by a certain structure The compressed videos will contain fewer data For example, with the H264 compression standard, the data contained in the compressed video includes 11 information about macroblock, motion vector, frame information, etc We call these data that the data in the compressed domain or video compression region Fig 1.2 shows the process of moving object detection methods by using the data in the compressed domain Figure 1.2 The process of moving object detection with data in the compressed domain In general, the amount of data in the video compression domain is much less than the data in the pixel domain The idea of using data in the compressed domain with the H264 compression standard for video analysis has also been investigated by some scientists around the world In order to be able to detect motion in the compressed video domain, we usually use two types of data They are the motion vector and the size (in bit) of the macroblock 1.2.1 Motion vector approaches A number of algorithms have been proposed to analyze video content in the H264 compressed domain, whose good performances have been obtained [9] [10] Zeng et al Study in [11] proposed a method to detect moving objects in H264 compressed videos based on motion vectors Motion vectors are extracted from the motion field and classified into several types Then, they are grouped into blocks through the Markov Random Field (MRF) classification process Liu et al [12] recognized the shape of an object by using a map for each object This approach is based on a binary partition tree created by macroblocks Cipres et al [13] presented a moving object detection approach in the H264 compressed domain based on fuzzy logic The motion vectors are used to remove the noises that appear during the encoding process and represent the concepts that describe the detected regions Then, the valid motion vectors are grouped into blocks Each of them could be identified as a moving object in the video scene The moving objects of each frame are described with common terms like shape, size, position, and velocity Mak et al [14] used the length, angle, and direction of motion vectors to track the objects by applying the MRF Bruyne et al [15] estimated the 12 reliability of motion vectors by comparing them with projected motion vectors from surrounding frames Then, they combined this information with the magnitude of motion vectors to distinguish foreground objects from the background This method can localize the noisy motion vectors and their effect during the classification can be diminished Wang et al [16] proposed a background modeling method using the motion vector and local binary pattern (LBP) to detect the moving object When a background block was similar to a foreground block, a noisy motion vector would appear To obtain a more reliable and dense motion vector field, the initial motion vector fields were preprocessed by a temporal accumulation within three inter frames and a 3×3 median filtering After that, the LBP feature was introduced to describe the spatial correlation among neighboring blocks This approach can reduce the time of extracting moving objects while also performing an effective synopsis analysis Marcus Laumer [17] proposed an approach to segment video frames into the foreground and background and, according to this segmentation, to identify regions containing moving objects The approach uses a map to indicate the "weight" of each (sub-)macroblock for the presence of a moving object This map is the input of a new spatiotemporal detection algorithm that is used to refine the weight that indicated the level of motion for each block Then, quantization parameters of macroblocks are used to apply individual thresholds to the block weights to segment the video frames The accuracy of the approach was approximately 50% To identify the human action, Tom et al [18] proposed a quick action identification algorithm The algorithm uses quantization parameters gradient image (QGI) and motion vectors with support vector machines (SVM) to classify the types of the actions The algorithm can also handle light, scale and some other environmental variables with an accuracy rate of 85% on the videos with resolution 176x144 It can identifies walking, running, etc Similarly, Tom, Rangarajan and his colleagues also used QGI and motion vector to propose a new method to classify human actions as the Projection Based Learning of the Metacognitive Radial Basis Functional Network (PBL-McRBFN) With the motion tracking problem, Biswas et al [19] propose a method for detecting abnormal actions by analyzing motion vector This method mainly relies on observing the motion vector to find the difference between abnormal actions and normal situations The classifier used here is the Gaussian Mixture Model (GMM) This approach base on their another approach [20] but improved it by using the direction of the motion vector The speed of approach when perform experimental is about 70fps Thilak et al [21] propose a Probabilistic Data 13 Association Filter that detects multiple target clusters This method can handle cases in which targets split into multiple clusters or clusters should be detected (classified) as a target Similarly, You et al [22] use the probabilistic spatiotemporal MB filtering to mark the macroblock as objects and then remove them from the noise The algorithm can track many objects with real-time accuracy but can only be applied in case of fixed camera and objects must be at least two macroblocks Kas et al [23] overcame the fixed camera problem using Global Motion Estimation and Object History Images to handle background movement However, the number of motion objects need to be small and the moving objects are not occupied most of the frame area 1.2.2 Size of Macroblock approaches The methods mentioned above share the trait of using motion vectors to detect moving objects However, since motion vectors are usually created at the video encoder to optimize video compression ratio, they not always represent the real motion in the video sequence As such, due to its coding-oriented nature, to detect moving objects, the motion vector fields must be preprocessed and refined to remove the noises So, Poppe et al [24] proposed an approach to detect moving objects in the H264 video by using the size of the macroblocks after encoding (in bit) To achieve Submacroblock-level (4×4) precision, the information from transform coefficients was also utilized The system achieved high execution speeds, up to 20 times faster than the motion vector-based related works An analysis was restricted to Predicted (P) frames, and a simple interpolation technique was employed to handle Intra (I) frames The whole algorithm was based on the assumption that the macroblocks that contains an edge of a moving object is more difficult to compress since it is hard to find a good match for those macroblocks in the reference frame(s) Base on Poppe’s idea, Vacavant et al [25] used the macroblock size to detect moving objects by applying the Gaussian mixture model (GMM) The approach can represent the distribution of macroblock sizes well Although the method of Poppe and Vacavant is good for removing the background motion noise, they cannot produce high motion detection results for videos in high spatial resolution (such as 1920 × 1080 or 1280 × 720) In case where the moving objects are large and they contain a uniform color region (such as a black car), then the size of macroblocks corresponding to the inside region of 14 the moving object will be very small (normally around zero), and using a filtering threshold or parameter (though very small) will not be effective In those cases, the algorithm will determine these regions to be background Chapter Summarization In this chapter showed the researchs about moving object detection in both pixel domain and compressed domain The approachs using data from pixel domain usually have high accuracy but taking a large number of computing resources and time The approachs using data in compressed domain have lower accuracy because the data in compressed domain usually contain less information In the next chapters, I will propose a method that can efficiently detect moving objects, especially in high spatial resolution video streams The method uses the data taken from the video compressed domain, including the size of the macroblocks to detect the skeleton of the moving object and the motion vectors to detect the detail of the moving object 15 CHAPTER METHODOLOGY Video compression standard h264 Before proposing the moving object detection method, this chapter will show some informations about H264, a popular video compression standard, which is used to encode and decode the surveillance video in the thesis This day, the installation of surveillance cameras in house became quite common Normally, video data from a surveillance camera over a long period of time usually has very huge size Consequently, videos need to be preprocessed and encoded before being used and transmitted over the network There are many recognized compression standards and widely used One of these is the H264 or MPEG-4 part 10 [26], a compression standard recognized by the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group 2.1.1 H264 file structure Normally, the video after being captured from the camera will be compressed using a common video compression standard such as H261, H263, MP4, H264/AVC, H265/HEVC, etc In the thesis, I encode and decode the video by using H264/AVC The H264 video codec or MPEG-4 part 10 is recognized by the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group Typically, an H264 file is splitted into packets called the Network Abstraction Layer Unit (NALU) [27], as shown in Fig 2.1 Figure 2.1 The structure of a H264 file The first NALU byte indicates the type of NALU The NALU type shows what the NALU's structure is It can be a slice or set parameters for decompression The meaning of the NALU in Table 2.1 16 Table 2.1 NALU types Type Definition 10 11 12 13 23 24 31 Undefined Slice layer without partitioning non IDR Slice data partition A layer Slice data partition B layer Slice data partition C layer Slice layer without partitioning IDR Additional information (SEI) Sequence parameter set Picture parameter set Access unit delimiter End of sequence End of stream Filler data Reserved Undefined Other than NALU, the rest of the NALU is called RBSP (Raw Byte Sequence Payload) RBSP contains data of SODB (String Of Data Bits) According to the specification document H264 (ISO/IEC 14496-10) if the SODB is empty (no bits are present), the RBSP is also empty The first byte of RBSP (left side) contains bits of SODB; The next byte of the RBSP will contain up to bits of SODB and continue until less than bits of SODB Figure 2.2 RBSP structure 17 A video will normally be divided into frames and the encoder will encode them one by one Each frame is encoded into slices Each slice is divided into Macroblock (MB) Typically, each frame corresponds to a slice, but sometimes a frame can be split into multiple slices The slices are divided into categories as shown in Fig 2.2 A slice consists of a header and a data section (Fig 2.3) The header of the slice contains information about the type of slice, the type of MB in the slice, the number of slice frames The header also contains information about the reference frame and quantitative parameters The data portion of the slice is the information about the macroblock Table 2.2 Slide types Type Description P-slice Consists of P-macroblocks (each macroblock is predicted using one reference frame) and/or I-macroblocks B-slice Consists of B-macroblocks (each macroblock is predicted using one or two reference frames) and/or I-macroblocks I-slice Contains only I-macroblocks Each macroblock is predicted from previously coded blocks of the same slice SP-slice Consists of P and/or I-macroblocks and lets you switch between encoded streams SI-slice It consists of a special type of SI-macroblocks and lets you switch between encoded streams P-slice B-slice I-slice SP-slice SI-slice Tải FULL (53 trang): https://bit.ly/3FkzN8W Dự phòng: fb.com/TaiHo123doc.net 18 Figure 2.3 Slide structure 2.1.2 Macroblock Tải FULL (53 trang): https://bit.ly/3FkzN8W Dự phòng: fb.com/TaiHo123doc.net The basic principle of a compression standard is to split the video into frame groups Each frame is divided into the basic processing units (For example, in the H264/AVC standard, it is Macroblock (MB) which is a region 16x16 pixels) Also, with some data regions carrying more detail, the MBs will be subdivided into smaller sub-macroblocks (4x4 or 8x8 pixels) Each MB after compression will contain the information used to recover the video later, including Motion vector, Residual value, Quantization parameter, etc as in Fig 2.4, where: • • • • • • ADDR is the position of Macroblock in a frame; TYPE is the Macroblock type; QUANT is the quantization parameter; VECTOR is Motion vector; CBP (Coded Block Pattern) show how to split MB into smaller blocks; bN is encoded data of residual of color channels (4 Y, Cr, Cb) Figure 2.4 Macroblock structure During decompression, the video decoder receives the compressed video data as a stream of binary data, decodes the elements and extracts the encoded information, including coefficients of variation, size of MB (in bit), motion 19 prediction information, and so on and perform the reverse transformation to restore the original image data 2.1.3 Motion vector With H264 compression, frame-based megabytes are predicted based on the information that has been transferred from the encoder to the decoder Usually, there are two ways of predicting frame prediction and inter-frame prediction Frame forecasting uses compressed image data in the same frame as the compressed macroblock and predicts inter-frame image data using previously compressed frames Interframe forecasting is accomplished through a predictive and compensatory motion process in which the motion predator retrieves the macroblock in the reference frame closest to the new macroblock and calculates the motion vector, this vector characterizes the shift of the new macroblock to encoding compared to the reference frame Referenced macroblocks are sent to the subtractor with the new macroblock that needs coding to find error prediction or residual signal, which will characterize the difference between the predicted macroblock and the actual macroblock The residual signal or prediction error will be converted to Discrete Cosine Transform and quantized to reduce the number of bits to be stored or transmitted These coefficients together with the motion vectors will be applied to the entropy compressor and the bit stream Video streams of binary data include conversion factors, motion prediction information, compressed data structure information, and more To perform video compression, one compares the values of the two frames A frame is used as a reference When we want to compress a MB at position i of a frame, the video compression algorithm tries to find the reference frame of a MB with the smallest value of MB compared to MB at position i Then, if MB is found in the reference frame at position j, the change between i and j is called the Motion vector (MV) of MB at position i (Fig 2.5) Normally an MV will consist of two values: x (the column position of MB) and y (row position of MB) 6815653 ... vision are taken from scientific articles related to the video analysis problem on the compression domain, determine the motion form on the compression domain of the video The videos for test... server for analysis The video analysis process needs a lot of features to describe different aspects of vision Typically, these features are extracted from the pixel values of each video frame... NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY NGUYEN MINH HOA MOTION ANALYSIS FROM ENCODED VIDEO BITSTREAM Major: Computer Science MASTER’S THESIS Supervisor: Dr Do Van Nguyen