Human Detection and Tracking for Video Surveillance A Cognitive Science Approach

With crimes on the rise all around the world, video surveillance is becoming more important day by day. Due to the lack of human resources to monitor this increasing number of cameras manually new computer vision algorithms to perform lower and higher level tasks are being developed. We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients the theory of Visual Saliency and the saliency prediction model Deep Multi Level Network to detect human beings in video sequences. Furthermore we implemented the k Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. We achieved a detection precision of 83.11% and a recall of 41.27%. We obtained these results 76.866 times faster than classification on normal images.Comment: ICCV 2017 Venice, Italy Pages 5 Figures

Automatic nesting seabird detection based on boosted HOG-LBP descriptors

Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution 1. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved. © 2011 IEEE

Deteksi Pejalan Kaki Pada Video Dengan Metode Fastest Pedestrian Detector in the West (FPDW)

— Pedestrian detection in video can be done offline for the purposes of analysis, such as analysis of pedestrian behavior, riots analysis and analysis of traffic accidents. In addition, pedestrian detection process is also done in real-time, such as for driving security systems, indoor navigation system, traffic control system at crossroads and so on. In this paper, a method FPDW (Fastest Pedestrian Detector in the West) will be tested to detect pedestrians in a video. Contribution of this paper is to add parameter of minimum confidence to the FPDW algorithm, to improve the accuracy of detection results. Experiments conducted on the video duration of 6 minutes 50 seconds extracted into 1 image per second.Keywords— pedestrian detector, FPDW, HOG, vide

A pedestrian detection method using the extension of the HOG feature

Development of an ITS (Intelligent Transport System) has drawn much attention from computer vision community in recent years. In particular, various techniques for detecting pedestrians automatically have been proposed by many researchers. Among them, the HOG feature proposed by Dalai & Triggs has gained much interest in the pedestrian detection. However, previous methods including the original HOG feature have not achieved satisfactory detection rates. In this paper, we propose an extension of the HOG feature, i.e., flexible choice of the number of bins and automatic definition of a cell size and a block size by parameterizing their scales. By comparative experiments, it was confirmed that the proposed method outperforms the previous methods in the performance of pedestrian detection.SCIS & ISIS 2014, December 3-6, 2014, Kitakyushu International Conference Cente