Abstract
Detection and tracking of pedestrian is a challenging task due to variable appearances, wide range of poses, and irregular motion of pedestrian along with motion of tracking camera under complex outdoor environmental conditions. In this paper, we propose an algorithm for pedestrian detection and tracking using HOG descriptors and particle filtering technique. A robust algorithm for pedestrian detection is proposed which works under nonlinear motion and overcomes occlusions. The performance of the above algorithm is tested for outdoor environment using standard dataset. The particle filter has benefits of handling nonlinear motion and occlusions, and they concentrate consecutively on the higher density regions of the state space and it is simple to realize which provides a robust tracking environment. Performance comparison of particle with conventional Kalman is also presented for the above-said cases.
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