Robust framework for human localization and detection in moving train carriage

Use of video surveillance cameras in public space is the recent solution to control vandalism acts and emergency incidents. Such type of incidents requires an urgent and an appropriate action by security personnel. Key problem for security personnel is to manage and monitor high volume of visual data. Since last decade, human detection for video surveillance systems is an emerging research area. There are several crucial factors that effect the performance of human/object detection, such as illumination changes, background clutter, dynamic background, occlusion, and camera orientation etc. In this paper, a hybrid approach is presented for the localization and detection of person inside a moving train with challenging environment. Our proposed framework contains two modules i.e. human localization and human detection. We proposed a hybrid approach using GMM background modeling for foreground extraction followed by head and face detection to be used as a clue for human detection. Along with head and face detection, Histogram of Oriented Gradient (HOG) feature representation is used for human localization. For detection, ensemble classifier outperforms SVM and KNN classifiers on BOSS Dataset (On Board Wireless Secure Video Surveillance) and achieved 90% accuracy.