Abstract
Visual tracking is a very important application in the field of computer vision. The tracking process can be formulated as a dynamic optimization problem, which can be solved by particle swarm optimization (PSO) algorithms. PSO algorithm with particle filter (PF) has been actively used in visual tracking. In this paper, we propose an improved resampling cellular quantum-behaved PSO (RScQPSO) algorithm, which is a probabilistic variant of PSO, and combine the PF to solve the tracking problem. The cQPSO algorithm can better keep the population diversity and balance the global and local search than PSO algorithm. For better tracking performance, we further improve the tracking algorithm by improving the particle initialization approach in cQPSO, resampling technique in PF as well as using the Gaussian mixture model in fitness assessment. Experimental results demonstrate that the proposed tracking algorithm is more effective and accurate, especially for the cases that the object has an arbitrary motion or undergoes large appearance changes, than the compared algorithms.
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Acknowledgement
This work was partially supported by the National Natural Science foundation of China (Grant Nos. 61673194, 61105128, 61170119, 61373055), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20131106), the Postdoctoral Science Foundation of China (Grant No. 2014M560390), the Fundamental Research Funds for the Central Universities, China (Grant No. JUSRP51410B), Six Talent Peaks Project of Jiangsu Province (Grant No. DZXX-025).
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Hu, J., Fang, W., Ding, W. (2016). Visual Tracking by Sequential Cellular Quantum-Behaved Particle Swarm Optimization Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_11
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DOI: https://doi.org/10.1007/978-981-10-3614-9_11
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