Title :
On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation
Author :
Niknejad, Hossein Tehrani ; Takeuchi, Akihiro ; Mita, Seiichi ; McAllester, David
Author_Institution :
Toyota Technol. Inst., Nagoya, Japan
fDate :
6/1/2012 12:00:00 AM
Abstract :
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%.
Keywords :
cameras; gradient methods; object detection; particle filtering (numerical methods); support vector machines; traffic engineering computing; vehicles; HOG; LSVM; deformable object model; deformation cost; detection algorithm; detection scores; global features; histograms of oriented gradients; latent support vector machine; likelihood estimation; likelihoods tracking; local features; multivehicle detection; on-road multivehicle tracking; particle filter; template compatibility; vehicle hypothesis regions; vehicle parts; vehicle-mounted monocular camera; Computational modeling; Correlation; Deformable models; Image color analysis; Tracking; Vectors; Vehicles; Intelligent vehicles; object detection; pattern recognition; tracking filters;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2012.2187894