On-road vehicle tracking using deformable object model and particle filter with integrated likelihoods
Akihiro Takeuchi, Seiichi Mita, David McAllester · 2010
This paper proposes a novel method for vehicle detection and tracking using a vehicle-mounted monocular camera. In this method, features of vehicles are learned as a deformable object model through the combination of a latent support vector machine (LSVM) and histograms of oriented gradients (HOG). The vehicle detector uses both global and local features as the deformable object model. Detected vehicles are tracked by using a particle filter with integrated likelihoods, such as the probability of vehicles estimated from the deformable object model and the intensity correlation between different picture frames. Tracking likelihoods are iteratively used as the a priori probability for the next frame. The experimental results showed that the proposed method can achieve an average vehicle detection rate of 98% and an average vehicle tracking rate of 87% with a false positive rate of less than 0.3%.