Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 13, Issue 2, Pages 748-758Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2012.2187894
Keywords
Intelligent vehicles; object detection; pattern recognition; tracking filters
Categories
Funding
- Research Center for Smart Vehicles, Toyota Technological Institute
Ask authors/readers for more resources
This paper proposes a novel method for multivehicle detection and tracking using a vehicle-mounted monocular camera. In the proposed method, the 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 (HOGs). The detection algorithm combines both global and local features of the vehicle as a deformable object model. Detected vehicles are tracked through a particle filter, which estimates the particles' likelihood by using a detection scores map and template compatibility for both root and parts of the vehicle while considering the deformation cost caused by the movement of vehicle parts. Tracking likelihoods are iteratively used as a priori probability to generate vehicle hypothesis regions and update the detection threshold to reduce false negatives of the algorithm presented before. Extensive experiments in urban scenarios showed that the proposed method can achieve an average vehicle detection rate of 97% and an average vehicle-tracking rate of 86% with a false positive rate of less than 0.26%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available