You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence 
Chapter Title:  INTRODUCTION 
1. Abstract of Cited Paper (BIB001):  Smart vehicle technologies such as ADAS are growing concern about. Especially, pedestrian and vehicle recognition system based on machine vision is a big issue. In this paper, we propose the hardwired HOG feature extractor circuit for real-time human and vehicle detection, and describe the hardware implementation results. Our HOG feature extractor supports weighted gradient value, 2D histogram interpolation and block normalization. We have used the simplified methods of the square root and division operation for the hardware implementation. Our HOG feature extractor circuit was verified on FPGA environment and can be processed 33 frames per seconds for 640×480 VGA images in real-time. 
2. Abstract of Cited Paper (BIB002):  In this paper, we propose an FPGA implementation of an object tracker inspired by the CAMSHIFT Algorithm that can be applied to VGA resolution images. We focus on architectural issues in designing such a system when constrained by the capabilities of commodity hardware and discuss a variety of area and speed tradeoffs relevant to low cost systems, with a particular focus on throughput and parallelism. We argue that while a system oriented towards vector processing offers a significant reduction in processing time, limitations in the acquisition rate of the sensor prevent us from realizing absolute time-savings. Thus, we consider the optimization in a different light - that processing time reductions afford room for additional processing and necessitate a focus on small area designs. The system targets the Altera Cyclone IV EP4CE115F29C7 FPGA found on the DE2-115 development board, with a Terasic D5M Digital Camera, and is pipelined to achieve a nominal maximum pixel clock rate of 100MHz. 
3. Abstract of Cited Paper (BIB003):  Detecting humans from video sequences is a key and difficult problem in computer vision. The problem becomes even more challenging when the camera is mobile, and thus, the background is not static. In this case, the traditional approach of background subtraction cannot be employed. Methods based on Histogram of Oriented Gradients (HOG) have been introduced and widely used for human detection due to reliable performance. Yet, these sliding window-based algorithms are computationally expensive to run on embedded platforms. In order to address this problem, we present a significantly faster moving human detection method that is based on fast motion segmentation, and uses HOG as the feature descriptor. First, edges are detected on two consecutive frames, and the difference between the edge images is computed. Then, a new edge-based frame alignment method is used to find the global minimum for motion compensation, and segment the motion region of interest (ROI). Instead of searching for the human(s) in the whole frame, the HOG features in sliding windows are calculated only in the ROI. Finally, Support Vector Machine (SVM) is used to classify human and non-human regions. Compared to the traditional method of using HOG and searching the whole frame, the proposed method significantly reduces the search region by motion segmentation, and speeds up the detection process. Experiments have been performed on three different scenarios, and the results show that the reduction in execution time of a frame can reach as high as 95.83%. 
4. Abstract of Cited Paper (BIB004):  In intelligent transportation systems, human detection is an important issue and has been widely used in many applications. Histograms of oriented gradients (HOG) are proven to be able to significantly outperform existing feature sets for human detection. In this paper, we present a low-cost high-speed hardware implementation for HOG feature extraction. The simulation shows that the proposed circuit can achieve 167 MHz with 153-K gate counts by using Taiwan Semiconductor Manufacturing Company 0.13-μm technology. Compared with the previous hardware architectures for HOG feature extraction, our circuit requires fewer hardware costs and achieves faster working speed. 
5. Abstract of Cited Paper (BIB005):  Histogram of oriented gradients (HOG) is considered as the most promising algorithm in human detection, however its complexity and intensive computational load is an issue for real-time detection in embedded systems. This paper presents a hardware accelerator for HOG feature extractor to fulfill the requirements of real-time pedestrian detection in driver assistance systems. Parallel and deep pipelined hardware architecture with special defined memory access pattern is employed to improve the throughput while maintaining the accuracy of the original algorithm reasonably high. Adoption of efficient memory access pattern, which provides simultaneous access to the required memory area for different functional blocks, avoids repetitive calculation at different stages of computation, resulting in both higher throughput and lower power. It does not impose any further resource requirements with regard to memory utilization. Our presented hardware accelerator is capable of extracting HOG features for 60 fps (frame per second) of HDTV (1080x1920) frame and could be employed with several instances of support vector machine (SVM) classifier in order to provide multiple object detection. 
6. Abstract of Cited Paper (BIB006):  Detecting and tracking people in video have a wide variety of applications in computer vision. However the development of robust methodologies for these purposes are challenging due to complexity of the scenes. In this paper, we propose a real-time algorithm for people detection and tracking by tridimensional depth estimation in indoor environment. The approach is based on people detection and stereo processing techniques for 3D depth estimation of the analyzed scene. The prototype implementation showed satisfactory results for people detection and tracking within pre-recorded video and also in real-time captured video by a stereo camera. The performance on standard hardware and using open source software library achieved a frame rate of up to 7 fps.

Literature Review:
Tracking people as they move through video sequences is one of the most basic and most important tasks in computer vision BIB007 ,  , BIB004 . It has numerous applications in surveillance vehicle tracking, motion capture, humancomputer interaction, robotics etc. Due to the complexity of human dynamic structures, the detection is not easy and straightforward. As a result, there are deferent detection and tracking techniques with different applications and capacities. The detection results may not represent actual values as always there are artifacts introduced during video capturing. Consequently, it is important to a filter in order to estimate the measured signals. Kalman filter is used in this paper as it is an optimal signal estimator that provides the estimation of a signal in a noise BIB001 ,  . To minimize the covariance of the above two techniques, a Kalman filter is used as filtering method. Finally, two methods of multi-sensor data fusion are implemented. For parallel and improved computational gain, some researchers in BIB005 , BIB002 , BIB006 , BIB003 proposed hardware implementations of HoG and Camshaft. Similarly, hardware-based data fusion algorithms are implemented in BIB001 . However, due to the complexity of human dynamic structure, there are numerous algorithm developed for detection, tracking, and multi-sensor data fusion. These different techniques provide different performances depending on the application. In this paper, comparative survey of HOG and CAMShift detectors, Kalman filter based tracking and multi-sensor fusion methods are presented.