Optimization for Particle Filter-Based Object Tracking in Embedded Systems Using Parallel Programming

Object tracking is a common task in computer vision, an essential part of various vision-based applications. After several years of development, object tracking in video is still a challenging problem because of various visual properties of objects and surrounding environment. Particle filter is a well-known technique among common approaches, has been proven its effectiveness in dealing with difficulties in object tracking. In this research, we develop an particle filter-based object tracking method using color distributions as features. Moreover, recently embedded systems have become popular because of the rising demand of portable, low-power devices. Therefore, we also try to deploy the particle filter-based object tracker in an embedded system. Because particle filter is a high-complexity algorithm, we will utilize computing power of embedded systems by implementing a parallel version of the algorithm. The experimental results show that parallelization can increase performance of particle filter when deployed in embedded systems.