Interactive teaching learning based optimization technique for multiple object tracking

In this paper, an Interactive Teaching Learning Based Optimization (In-TLBO) algorithm is proposed for tracking multiple objects with several challenges. The performance of the four other competitive approaches, such as the Mean Shift (MS), Particle Swarm Optimization (PSO), Sequential PSO (SPSO), and Adaptive Gaussian Particle Swarm Optimization (AGPSO) are investigated for comparison. The quantitative and qualitative analyses of these approaches have been performed to demonstrate their efficacy. The comparison of various performance measures includes the convergence rate, tracking accuracy, Mean Square Error (MSE) and coverage test. To assess the dominance of the proposed In-TLBO approach, Sign and Wilcoxon test are also performed. These two non-parametric tests reveal considerable advancement of the proposed Interactive TLBO (In-TLBO) over other four competitive approaches. In-TLBO shows significant improvement over the MS and PSO algorithms with a level of significance α = 0.05, and over SPSO, with a level of significance α = 0.1 by considering detection rate as winning parameter. The analyses of comparative results demonstrate that the proposed approach effectively tracks similar objects in the presence of many real time challenges.