Pedestrians Detection and Part-Based Tracking by Adaptive Segmentation, Neural Network and Kalman Filter

Abstract

Abstract In this paper authors have proposed a robust pedestrian tracking algorithm for stationary surveillance videos by combining the multiple adaptive segmentation, computationally efficient feature set, Neural Network and Kalman Filter. In the proposed method, human detection has been performed by the use of an adaptive moving average background model with supportive secondary model, which accurately works in changing environment. The parts-based human model has been used with novel and robust feature set for efficient parts-based human recognition. These features are concise, invariant to pose changes and deformation. Pedestrian recognition has been performed by neural network. Visual tracking has been performed by well known Kalman Filter to estimate the target pedestrian position in each next frame. The algorithm is tested on different surveillance videos and results show that proposed pedestrian recognition and tracking method is robust and efficient than similar work done earlier.