Human Detection, Tracking and Trajectory Extraction in a Surveillance Camera network

Abstract

This paper proposes human tracking and recognition method in a camera network. Human matching in a multi-camera surveillance system is a fundamental issue for increasing the accuracy of recognition in multiple views of cameras. In camera network, videos have different characteristics such as pose, scale and illumination. Therefore it is necessary to use a hybrid scheme of scale invariant feature transform to detection and recognition human's behaviors. The main focus of this paper is to analyze activities for tracking and recognition humans to extract trajectories. Extracting the trajectories help to detect abnormal behavior which may be occluded in single- camera surveillance. KEYWORDS: Camera network, Multi-camera surveillance, Human's behavior, Trajectories extraction. I. INTRODUCTION Tracking and behavior recognition are two fundamental tasks in video surveillance systems which are widely employed in commercial applications for purposes of statistics gathering and processing. The number of cameras and complexity of surveillance systems have been continuously increasing to have better coverage and accuracy. Multi-camera systems become increasingly attractive in machine vision. Applications include multi view object tracking, event detection, occlusion handling and etc. In this paper, we develop method for tracking and recognition by a traffic video surveillance system of two cameras with a partially overlapping field of view. This paper is organized as follows: an overview of the past works in section2. Our proposed architecture and algorithm is presented in section3. Results of subjective evaluations and objective performance measurements with respect to Ground-truth are presented in section4. Section5 contains the conclusion.