Abstract
This paper proposes a novel particle filter algorithm for vehicle tracking, which feeds observation information back to state model and integrates block symmetry into observation model. In view of the proposal distribution in traditional particle filter without considering the observation data, a new state transition model which takes the observation into account is presented, so that the allocation of particles is more familiar with the posterior distribution. To track the vehicles in background with similar colors or under partial occlusion, block symmetry is proposed and introduced into the observation model. Experimental results show that the proposed algorithm can improve the accuracy and robustness of vehicle tracking compared with traditional particle filter and Kernel Particle Filter.
Highlights
In recent years, the traffic pressure in some cities has become higher and higher, and the role of the intelligent transportation system (ITS) is more and more important
Symmetry Asymmetry (b) Symmetry of every row Different from [18], we propose the block symmetrybased observation model to accurately track the vehicles under partial occlusion
This paper proposes a novel state transition model based on observation feedback and an observation model which fused block symmetry and color features in the particle filter framework
Summary
The traffic pressure in some cities has become higher and higher, and the role of the intelligent transportation system (ITS) is more and more important. Particle filter is a kind of popular and robust approach for vehicle tracking It is called Sequential Monte Carlo method [3] or Condensation algorithm [4]. Zhu et al [7] combined particle filter and grey prediction model and proposed a novel visual tracking algorithm, called GMPF. Rezaee et al [10] fused color, edge, texture, and motion cues in particle filter algorithm for vehicle tracking. Gao et al [12] constructed proposal distribution by use of state partition technique and parallel extended kalman filter and fused adaptively color model and shape model in particle filter framework to enhance the performance of object tracking. Niknejad et al [14] proposed a method for multiple vehicles tracking combining a deformable object model with particle filter.
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