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

Mai Thanh Nhat Truong,Sanghoon Kim

doi:10.1186/s13673-016-0082-1

Abstract

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.

Highlights

Object tracking has important roles in many vision-based applications such as traffic monitoring [1], surveillance systems [2], and recently, augmented reality [3]
We introduce a parallel implementation of particle filter algorithm, for the purpose of enhancing the performance of the particle filter based object tracking method when deployed in embedded systems
Experimental results we present the performance of the object tracking method

Summary

Introduction

Object tracking has important roles in many vision-based applications such as traffic monitoring [1], surveillance systems [2], and recently, augmented reality [3]. Object tracking can be more difficult because of object shapes, light conditions, occlusions, sudden change in object motions, camera motions, etc. Owing to various difficulties that cannot be solved simultaneously, object tracking methods are usually designed to track objects with specific properties in certain environments [4]. One object tracker produces good results in various environments under different lighting conditions, but produces low accuracy results when the target shape or silhouette is changed because of camera angles. One tracking method can predict target movement accurately, but it may fail when tracking bouncing objects as a result of sudden changes in movement direction. Object tracking is still a high-complexity and time-consuming task. The complexity is increased when the tracking task is performed in environments with complex surroundings, or the requirement is to track objects with various appearances

Objectives

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Results

Conclusion