文章基本信息

标题：Pedestrian Tracking based on Camshift with Kalman Prediction for Autonomous Vehicles
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作者：Lie Guo ; Linhui Li ; Yibing Zhao and Zongyan Zhao 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2016
卷号：13
DOI：10.5772/62758
语种：English
出版社：SAGE Publications
摘要：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.
关键词：Autonomous Vehicles; Pedestrian Tracking; Kalman Filter; Camshift Algorithm; Colour Probability Distribution