Tài liệu Luận văn cntt motion analysis from encoded video bitstream

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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 Công 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 ...................................................................................... 1 ABBREVIATIONS ............................................................................................... 3 List of Figures ....................................................................................................... 4 List of Tables ......................................................................................................... 5 INTRODUCTION ................................................................................................. 6 CHAPTER 1. LITERATURE REVIEW ............................................................ 9 Moving object detection in the pixel domain .......................................... 9 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 2. 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 2 Chapter Summarization ......................................................................... 28 CHAPTER 3. 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 3 ABBREVIATIONS MB Macroblock MV Motion vector NALU Network Abstraction Layer Unit RBSP Raw Byte Sequence Payload SODB String Of Data Bits 4 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 5 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 1 .................................... 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 6 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 7 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 3 chapters with the following main contents: Chapter 1 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 2 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 3 shows the results of method including an application using proposed method and experimental results. 9 CHAPTER 1. 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 do 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 2. 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.
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