Object Tracking based on Quantum Particle Swarm Optimization

Abstract

In Computer Vision tracking of moving object in a real life scene is considered as a very challenging problem. Many factors such as illumination of light, noise, occlusion, sudden start and stop of moving object, shading etc. are associated with the task of tracking an object in a real scene under dynamic background(varying background) as well as static background(fixed background). In this paper we present a new object tracking algorithm using Quantum particle swarm optimization (QPSO).QPSO essentially deals with the dominant points of an object to be tracked. The novelty of our approach is that QPSO with a set of dominant points as stated above can be successfully applied for object tracking with both variable background as well as static background. Thus a unified attempt has been made for object tracking in a real life scene. In our approach we first detect the dominants points of objects to be tracked, then a group of particles form a swarm are initialized randomly over the image search space. It start searching the curvature connected between two consecutive dominant points until they satisfy a fitness criterion. As the curvature moves and the curvature movement is tracked by the swarm throughout the video and eventually when the swarm reaches optimal solution, a bounding box drawn based on particles final position. Experimental results demonstrate that this proposed QPSO based method works efficiently and effectively for object tracking in both dynamic and static environments. A comparative study shows that QPSO based tracking algorithm, on an average, runs 90% faster than PSO based tracking algorithm.