An Efficient Target Tracking Algorithm Based on Particle Filter and Genetic Algorithm

Document Type : Original Article

Authors

1 Artificial Intelligence Group, Faculty of Computer Engineering and Information Technology, Islamic Azad University, Qazvin, Iran

2 Electrical Engineering Department, Imam Khomeini International University, Qazvin, Iran

Abstract

In this paper, we propose an efficient hybrid Particle Filter (PF) algorithm for video tracking by employing a genetic algorithm to solve the sample impoverishment problem. In the presented method, the object to be tracked is selected by a rectangular window inside which a few numbers of particles are scattered. The particles’ weights are calculated based on the similarity between feature vectors of the scattered particles and that of the central particle. Before the resampling stage of PF algorithm, particles with the highest weights are evolved using a genetic algorithm. The evolved particles’ coordinates are transferred to the next frame by a random walk model, and the rectangle involving new particles is specified. Moreover, we utilize the idea of partitioning (selecting parts of target in the first frame with a distinct color/texture) and reducing image size to decrease the number of particles. The partitioning idea also helps our method in resolving the occlusion problem. Simulation results demonstrate the outperformance of the suggested approach comparing with other methods in terms of precision and tracking time when it encounters with the challenges such as full and partial occlusions, illumination and scale variations, fast motions, and color similarity between the object and background.

Keywords


1. Toloei, A., Niazi, S., “Estimation  of  LOS  rates  for  target
tracking  problems  using  EKF  and  UKF  algorithms-a
comparative study”, International Journal of Engineering,
Transactions B: Applications, Vol. 28, No. 2, (2015), 172-178.
2. Mahdavi, M., Shahrouzi, S.N., Hasanzadeh, R., “A novel method
for tracking moving objects using block-based similarity”,
International Journal of Engineering, Transactions B:
Applications, Vol. 22, No. 1, (2009), 35-42.
3. Li, S., Zhao, S., Cheng, B., Zhao, E., Chen, J., “Lightweight
Particle Filter for Robust Visual Tracking”, IEEE Access, Vol. 6,
(2018), 32310-32320.
4. Elafi, I., Jedra, M., Zahid, N., “Tracking objects with cooccurrence
matrix and particle filter in infrared video sequences”,IET Computer Vision,Vol.12,No.5,(2018),634-639.
5. Gordon, N.J., Salmond, D.J., Smith, A.F., “Novel approach to
nonlinear/non-Gaussian Bayesian state estimation”, IEE Proc. FRadar
and Signal Process.,Vol.140,No.2,(1993),107-113.
6. Walia, G.S., Kapoor, R., “Intelligent video target tracking using
an evolutionary particle filter based upon improved cuckoo
search”, Expert Systems with Applications, Vol. 41, No. 14,
(2014), 6315-6326.
7. Gao, M.L., Li, L.L., Sun, X.M., Yin, L.J., Li, H.T., Luo, D.S.,
“Firefly algorithm based particle filter method for visual
tracking”, Optik-International Journal for Light and Electron
Optics, Vol. 126, No. 18, (2015), 1705-1711.
8. Han, H., Ding, Y.S., Hao, K.R., Liang, X., “An evolutionary
particle filter with the immune genetic algorithm for intelligent
video target tracking”, Computers & Mathematics with
Applications, Vol. 62, No. 7, (2011), 2685-2695.
9. Zhao, J., Li, Z., “Particle filter based on Particle Swarm
Optimization resampling for vision tracking”, Expert Systems
with Applications, Vol. 37, No. 12, (2010), 8910-8914.
S. Sadegh Moghadasi and N. Faraji / IJE TRANSACTIONS A: Basics  Vol. 32, No. 7, (July 2019)   915-923                        923
10. Zhou, Z., Wu, D., Zhu, Z., “Object tracking based on Kalman
particle filter with LSSVR”, Optik, Vol. 127, No. 2, (2016), 613619.
11. Zhao, B., Hu, J.W., Ji, B., “An improved particle filter based on
genetic resampling”, International Conference on Automation,
Mechanical Control and Computational Engineering, China,
(2015).
12. Tong, G., Fang, Z., Xu, X., “A Particle Swarm Optimized Particle
Filter for Nonlinear System State Estimation”, International
Conference on Evolutionary Computation, Vancouver, Canada,
(2006).
13. Hlinka, O., Hlawatsch, F., Djurić, P.M., “Likelihood consensusbased
distributed particle filtering with distributed proposal
density adaptation”, ICASSP, (2012).  
14. Hlinka, O., Sluciak, O., Hlawatsch, F., Djuric, P.M., Rupp, M.,
“Likelihood consensus and its application to distributed particle
filtering”, IEEE Trans. on Signal Process., Vol. 60, No. 8, (2012),
4334-4349.
15. Pocock, J.A., Dance, S. L., Lawless, A.S., “State estimation using
the particle filter with mode tracking”, Computers & Fluids, Vol.
46, No. 1, (2011), 392-397.
16. Gao, M., Zhang, H., “Sequential Monte Carlo methods for
parameter estimation in nonlinear state-space models”,
Computers & Geosciences, Vol. 44, (2012), 70-77.
17. Kristan, M., Kovacic, S., Leonardis, A., Pers, J., “A two-stage
dynamic model for visual tracking”, IEEE Trans. on Systems,
Man, and Cybernetics, Part B (Cyber.), Vol. 40, No. 6, (2010),
1505-1520.
18. Del Bimbo, A., Dini, F., “Particle filter-based visual tracking with
a first order dynamic model and uncertainty adaptation”,
Computer Vision and Image Understanding, Vol. 115, No. 6,
(2011), 771-786.
19. Comaniciu, D., Ramesh, V., Meer, P., “Kernel-based object
tracking”, IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol. 25, No. 5, (2003), 564-577.
20. Park, J., Anh, L.T., Lee, G., “Knowledge discovery in signal
processing and multi-media color image segmentation using
adaptive mean shift and statistical model-based method”, In
International Conference on Fuzzy Systems and Knowledge
Discovery, Vol. 4, (2007).
21. Shen, C., Kim, J., Wang, H., “Generalized kernel-based visual
tracking”, IEEE Trans. on Circuits and Systems for Video
Technology, Vol. 20, No. 1, (2010), 119-130.       
22. Fisher, R., “CAVIAR Test Case Scenarios”,
http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1, (2007).
23. Zajc, L.C., “Sequence gymnastics”, From the ICCV2011 paper:
An adaptive coupled-layer visual model for robust visual
tracking. http://www.vicos.si/Research/LocalGlobalTracking,
(2011).  
24. PETS’2000: Outdoor people and vehicle tracking (single
camera). http://www.cvg.rdg.ac.uk/slides/pets.html.    
25. Lazarevic-McManus, N., Renno, J.R., Makris, D., Jones, G.A.,
“An object-based comparative methodology for motion detection
based on the F-Measure”, Computer Vision and Image
Understanding, Vol. 111, No. 1, (2008), 74-85.