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.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[3]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[4]  Fawzi Nashashibi,et al.  Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Ingemar J. Cox,et al.  A review of statistical data association techniques for motion correspondence , 1993, International Journal of Computer Vision.

[7]  Rita Cucchiara,et al.  Vehicle Detection under Day and Night Illumination , 1999, IIA/SOCO.

[8]  Christopher K. I. Williams,et al.  Pascal Visual Object Classes Challenge Results , 2005 .

[9]  A. Doucet On sequential Monte Carlo methods for Bayesian filtering , 1998 .

[10]  I. Cabani,et al.  Color-based detection of vehicle lights , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[11]  Chin-Hsing Chen,et al.  Vehicle Detection and Counting by Using Headlight Information in the Dark Environment , 2007, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007).

[12]  Thomas Schamm,et al.  On-road vehicle detection during dusk and at night , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[13]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.