A Spectral Clustering and Kalman Filtering Based Objects Detection and Tracking Using Stereo Vision with Linear Cameras

3D scene based objects detection and tracking is a central problem in many intelligent transportation applications. Dynamic stereo vision is the known approach to solve this problem. It consists in detecting and tracking objects from their reconstructed features using stereo images. This paper proposes a new method for detecting and tracking objects using stereo vision with linear cameras. Edge points extracted from the stereo linear images are first matched to reconstruct points that represent the objects in the scene. To detect the objects, a clustering process based on a spectral analysis is then applied to the reconstructed points. The obtained clusters are finally tracked throughout their center of gravity using Kalman filtering and a Nearest Neighbour based data association algorithm. Experimental results using real stereo linear images are shown to demonstrate the effectiveness of the proposed methods for obstacle detection and tracking in front of a vehicle.