On-road vehicle tracking using deformable object model and particle filter with integrated likelihoods

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%.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  K. Fuerstenberg,et al.  Pedestrian Recognition and Tracking of Vehicles using a vehicle based Multilayer Laserscanner , 2003 .

[3]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[4]  Zehang Sun,et al.  On-road vehicle detection using evolutionary Gabor filter optimization , 2005, IEEE Transactions on Intelligent Transportation Systems.

[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]  A Two-Stage Approach to People and Vehicle Detection With HOG-Based SVM , 2006 .

[7]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  N. Given.  Benchmarking Haar and Histograms of Oriented Gradients features applied to vehicle detection , 2007 .

[9]  Alberto Broggi,et al.  Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion , 2007, IEEE Transactions on Intelligent Transportation Systems.

[10]  Klaus Dietmayer,et al.  3D vehicle detection using a laser scanner and a video camera , 2008 .

[11]  Fabien Moutarde,et al.  Real-time visual detection of vehicles and pedestrians with new efficient adaBoost features , 2008 .

[12]  Seiichi Mita,et al.  Car detection using multi-feature selection for varying poses , 2009, 2009 IEEE Intelligent Vehicles Symposium.

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