Robust Motion Detection and Tracking of Moving Objects using HOG Feature and Particle Filter

Detection and tracking of moving objects has gained significant importance due to intense technological progress in the field of computer science dealing with video surveillance systems. Human motion is generally nonlinear and non-Gaussian and thus many algorithms are not suitable for tracking. One of the applications to maintain universal security is crowd control. The main problem of video surveillance is continuous monitoring with regard to crime prevention. For security monitoring of live surveillance systems, target identification and tracking strategies can automatically send warnings to monitoring officers. In this paper, we propose a robust tracking of a specified person using the individuals' feature. The proposed method to determine automatic detection and tracking combines Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The Histogram oriented Gradient features are applied to single detection window for the identification of human area, after we use particle filters for robust specific people tracking using color and skin color based on the characteristics of a target individual. We have been improving the implementation, evaluation system of our proposed methods. In our systems, for experiments, we choose structured crowded scenes. From our experimental results, we have achieved high accuracy detection rates and robust motion tracking for specific targets