Vehicle detection and tracking at nighttime for urban autonomous driving

This paper proposes a method for on road detecting and tracking of multi vehicles at nighttime in urban environment. The features of vehicles including root and part filters are learned as a weighted deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). Detected vehicles are tracked through a particle filter which estimates near optimum likelihoods by calculating the maximum HOG features compatibility for both root and parts of the tracked vehicles. Tracking likelihoods are iteratively used as a priori probability to generate vehicle hypothesis regions. Extensive experiments with close range IR camera in urban scenarios showed that the efficiency of the proposed method for detecting and tracking of multi vehicles at night time.