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Linear stereo vision based objects detection and tracking using spectral clustering

Objects detection and tracking is a key function for many applications like video surveillance, robotic, intelligent transportation systems,...etc. This problem is widely treated in the literature in terms of sensors (video cameras, laser range finder, Radar) and methodologies. This paper proposes a new approach for detecting and tracking objects using stereo vision with linear cameras. After the matching process applied to edge points extracted from the images, the reconstructed points in the scene are clustered using spectral analysis. The obtained clusters are then tracked throughout their center of gravity using a Kalman filter and a Nearest Neighbour (NN) based data association algorithm. The approach is tested and evaluated on real data to demonstrate its effectiveness for obstacle detection and tracking in front of a vehicle. This work is a part of a project that aims to develop advanced driving aid systems, supported by the CPER, STIC and Volubilis programs.