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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.

Abstract Visual odometry provides planetary exploration rovers with accurate knowledge of their position and orientation, which needs effective feature tracking results, especially in barren sandy terrains. In this paper, a stereovision based odometry algorithm is proposed for a lunar rover, which is composed of corner extraction, feature tracking and motion estimation. First, a morphology based image enhancement method is studied to guarantee enough corners are extracted. Second, a Random Sample Consensus (RANSAC) algorithm is proposed to make a robust estimation of the fundamental matrix, which is the basic and critical part of feature matching and tracking. Then, the 6 degrees of freedom rover position and orientation is estimated by the RANSAC algorithm. Finally, experiments are performed in a simulated lunar surface environment using a prototype rover, which have confirmed the feasibility and effectiveness of the proposed method.