An efficient hybrid framework for visual tracking using Exponential Quantum Particle Filter and Mean Shift optimization

Abstract

Visual object tracking is a key component in many computer vision applications. In real time visual tracking, abrupt changes in speed and direction of the object are demanding challenges. In this paper, we present an efficient visual tracking framework which can efficiently handle the above challenge, i.e., abrupt motion of the object. The framework is formulated by hybridizing the proposed Exponential Quantum Particle Filter (EQPF) with the traditional Mean-Shift (MS) optimization for efficient computation in object tracking task. The efficacy of EQPF in estimating the abrupt changes in functions, is tested on the standard Quail function, and then it has been successfully applied in object tracking algorithm. The effective multi-modal propagation strategies of Quantum Particle Filter (QPF) enables the tracker to handle the abrupt changes in speed and direction, whereas, the hybridization with MS enhances the computational efficiency by reducing the number of particles. Performance of the proposed method is assessed by experimenting on different publicly available challenging sequences. Both the subjective and objective evaluations are carried out to validate the superiority of the proposed tracking method over other state-of-the-art methods.