Particle Filter Based Object Tracking Research Articles

Parallel implementation of color-based particle filter for object tracking in embedded systems

Recently, embedded systems have become popular because of the rising demand for portable, low-power devices. A common task for these devices is object tracking, which is an essential part of various applications. Until now, object tracking in video sequences remains a challenging problem because of the visual properties of objects and their surrounding environments. Among the common approaches, particle filter has been proven effective in dealing with difficulties in object tracking. In this research, we develop a particle filter based object tracking method using color distributions of video frames as features, and deploy it in an embedded system. Because particle filter is a high-complexity algorithm, we utilize computing power of embedded systems by implementing a parallel version of the algorithm. The experimental results show that parallelization can enhance the performance of particle filter when deployed in embedded systems.

Robust Visual Tracking via Exclusive Context Modeling.

In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple group dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L1 tracker is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.

On the Accuracy of Particle Filter-Based Object Tracking

In particle filter framework, there are many parameters need to be set to have the object tracking work best. On the other hand, the success of an object tracking is also determined by the characteristics of the video being used. Different videos might require different values of parameters to obtain best performance. This paper investigates thoroughly the effect of parameters in object tracking within particle filter framework for videos with different characteristics. We also propose a new method in selecting particle number and matrix value that used in propagation model. This work finds best parameters for video with particular characteristics and contributes in providing guideline of parameter setting in particle filter-based object tracking.

A Fast Object Tracking Approach Based on the Motion Vector in a Compressed Domain

Particle set sampling and weighting are both at the core of particle filter-based object tracking methods. Aiming to optimally represent the object's motion state, a large amount of particles - in the classical particle method - is a prerequisite. The high-cost calculation of these particles significantly slows down the convergence of the algorithm. To this problem, a prior approach which originated from the process of video compressing and uncompressing is introduced to optimize the phase of particle sampling, making the collected particles centre on and cover the object region in the current image. This advantage dramatically reduces the number of particles required by the regularized particle sampling method, solving the problem of the high computational cost for tracking objects, while the performance of the algorithm is stable.

An Efficient Particle Filter–based Tracking Method Using Graphics Processing Unit (GPU)

Particle filter has been proven very robust in handling non-linear and non-Gaussian problems and has been widely used in the area of object tracking. One of the main problems in particle filter-based object tracking is, however, its high computational cost induced by the most time-consuming stage of measurement model computation. This paper makes progress in resolving the problem by proposing an efficient particle filter-based tracking algorithm using color information. First, a compact color cooccurrence histogram is presented, which considers both spatial and color information and can effectively represent color distribution with a very small number of histogram bins. The paper also introduces integral images by which the cooccurrence histogram can be obtained with simple array reference operations. However, the construction of the integral images on the CPU may be computationally expensive. Hence, this paper develops parallel algorithms on a desktop Graphics Processing Unit (GPU), which accomplishes the integral images construction and cooccurrence histogram computation after bin index determination. The resulting algorithm is quite efficient and has better performance than the traditional histogram-based tracking algorithm. The tracking time of the proposed algorithm increases insignificantly with the growth of particle number, and it remains consistent among varying image sequences and stable throughout all frames in the same image sequence due to its irrelevance to object size. Experiments in diverse image sequences validate our conclusions.