Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> Pros and cons <s> Pedestrian recognition on embedded systems is a challenging problem since accurate recognition requires extensive computation. To achieve real-time pedestrian recognition on embedded systems, we propose hardware architecture suitable for HOG feature extraction, which is a popular method for high-accuracy pedestrian recognition. To reduce computational complexity toward efficient hardware architecture, this paper proposes several methods to simplify the computation of HOG feature extraction, such as conversion of the division, square root, arctangent to more simple operations. To show that such simplifications do not spoil the recognition accuracy, the detection performance is also evaluated using a support vector machine. Moreover, we implement the proposed architecture on an ALTERA Stratix II FPGA using Verilog HDL to evaluate the circuit size and the processing performance of the proposed architecture. Implementation results show that real-time processing for 30 fps VGA video can be achieved if 10 instances of the proposed hardware are used in parallel. <s> BIB001 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> Pros and cons <s> Both detection and tracking people are challenging problems, because the human body is non-rigid and there is occasion between the body block. In general, human detection is a prerequisite for human tracking, and tracking has no effect on human detection. However, a novel approach is proposed for human detection and tracking in this paper, changing this situation. First, improved HOG is used to extract human features in the image. Second, we make the relationship between human detection and tracking closer-detection is not only the prerequisite of tracking and it also benefits from tracking. Finally, the Kalman filter is introduced into detecting and tracking people. Our experiments have demonstrated that such a method reduces detection time, and improves human detection and tracking accuracy. <s> BIB002 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> Pros and cons <s> For improving accuracy and robust property of human detection, fusion of image sequences captured from visible-thermal sensors is lucrative. Instead of performing it in pixel-level directly, we try to fuse object features by a novel image sequence fusion algorithm based on gradient feature (GFIF). The GFIF algorithm calculate gradients of input images to form a joint histograms of Oriented Gradient (HOG) descriptor, and these fused features are used to train linear support vector machine (SVM). In our experiment, a color-thermal surveillance dataset is adopted and several multi-resolution fusion algorithms are tested for comparing. By the Detection Error Tradeoff (DET) curves, the GFIF algorithm shows its superiority. <s> BIB003 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> Pros and cons <s> 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. <s> BIB004 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> Pros and cons <s> 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. <s> BIB005
The HOG detectors gives better results both in terms of the number of detected peoples and accuracy of the tracked two people with respect the ground truth. The limitation of HOG is computation speed. As the algorithm works based on local normalized gradient, it is very slow. On the other hand, as the CAMshift was applied on background subtracted image, it tracks only the person in high motion as there is high variation in the pixel intensity (hue) around this region. The CAMshift is very fast but not gives accurate tracking result. Both the above detectors are not invariant to occlusion. The MSE of both detectors is computed with respect to the ground truth tracking for the man going out from the shop as shown in Fig. 3 and Table III . The result shows that the HOG detection method provides better performance than the CAMshift. Even though HOG detectors give better results both in terms of the number of detected people and accuracy of the position of the detected people, it has low speed and is not robust for occlusion. To overcome the problem related to computational speed, Authors in BIB001 , BIB004 , BIB005 ,  have proposed hardware based implementation of HOG for people tracking. However, there are always some outliers and variance in the detection and can be improved by applying a filter. Kalman filter BIB002 has been proposed as an efficient filtering technique to predict the next detection and improve the overall tracking. One way to minimize the problems related to occlusion is to use multi-camera and use the multi-camera fusion techniques as proposed in BIB003 .