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%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Takeuchi, A (author) / Mita, S (author) / McAllester, D (author)


    Publication date :

    2010-06-01


    Size :

    1167164 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    On-Road Vehicle Tracking Using Deformable Object Model and Particle Filter with Integrated Likelihoods, pp. 1014-1021

    Takeuchi, A. / Mita, S. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010



    On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

    Tehrani Niknejad, Hossein / Takeuchi, Akihiro / Mita, Seiichi et al. | IEEE | 2012


    Gibbs likelihoods for Bayesian tracking

    Roth, S. / Sigal, L. / Black, M.J. | IEEE | 2004


    On-Road Vehicle Tracking Using Part-Based Particle Filter

    Fang, Yongkun / Wang, Chao / Yao, Wen et al. | IEEE | 2019