Indexed by:期刊论文

Date of Publication:2016-06-08

Journal:INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS

Included Journals:SCIE、EI

Volume:13

Issue:3

ISSN No.:1729-8814

Key Words:Autonomous Vehicles; Pedestrian Tracking; Kalman Filter; Camshift Algorithm; Colour Probability Distribution

Abstract:Pedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize a Kalman filter to predict the possible position of the pedestrian centroid in the future frame. A Camshift tracking algorithm is used to track the pedestrian in the specific search window of the next frame based on the prediction results. The actual position of the pedestrian centroid is output from the Camshift tracking algorithm to update the gain and error covariance matrix of the Kalman filter. Experimental results in real traffic situations show the proposed pedestrian tracking algorithm can achieve good performance even when they are partly occluded in inconsistent illumination circumstances.