Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> HOG for People Detection <s> We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. <s> BIB001 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> HOG for People Detection <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> BIB002 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> HOG for People Detection <s> 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%. <s> BIB003 </s> Comparative survey: People detection, tracking and multi-sensor Fusion in a video sequence <s> HOG for People Detection <s> Human detection in a video surveillance system has vast application areas including suspicious event detection and human activity recognition. In the current environment of our society suspicious event detection is a burning issue. For that reason, this paper proposes a framework for detecting humans in different appearances and poses by generating a human feature vector. Initially, every pixel of a frame is represented as an incorporation of several Gaussians and use a probabilistic method to refurbish the representation. These Gaussian representations are then estimated to classify the background pixels from foreground pixels. Shadow regions are eliminated from foreground by utilizing a Hue-Intensity disparity value between background and current frame. Then morphological operation is used to remove discontinuities in the foreground extracted from the shadow elimination process. Partial occlusion handling is utilized by color correlogram to label objects within a group. After that, the framework generates ROIs by determining which of the foregrounds represent human by considering conditions related to human body. Finally, Histogram of Oriented Gradients (HOG) feature is extracted from ROI for classification. Various videos containing moving humans are utilized to evaluate the proposed framework and presented outcomes demonstrate the adequacy. <s> BIB004
HOG object or people detection technique works based on locally contrast normalized histogram of orientation gradients. Local object appearance and shape are characterized by the distribution of local intensity gradients or edge directions BIB001 . The HOG detection method is proposed used for people detecting people in video sequence in BIB003 , BIB001 , BIB004 ,  and BIB002 . HOG works based on local contrast normalized histogram of orientation gradients. Local object appearance and shape are characterized by the distribution of local intensity gradients or edge directions BIB001 . 