A New Approach to Improve Tracking Performance of Moving Objects with Partial Occlusion.
محورهای موضوعی : Journal of Computer & RoboticsZahra Sahraei 1 , Amir Masoud Eftekhari Moghadam 2
1 - MSc Student of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University
2 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin
کلید واژه: Object tracking, Particle Filter, Salient Feature Points, Partial Occlusion,
چکیده مقاله :
< p>Tracking objects in video images has attracted much attention by machine vision and image processing researchers in recent years. Due to the importance of the subject, this paper presents a method for improving object tracking tasks with partial occlusion, which increases the efficiency of tracking. The proposed approach first performs a pre-processing and extracts the tracking targets from the image. Then the salient feature points are extracted from the targets that are moving objects. In the next step, the particle filter is used for tracking. The final steps are modifying points and updates. A new approach is used to determine the speed of the feature points because the speed of some points can be out of range and this causes errors in tracking especially when there is occlusion. The location of the new points is corrected and updated using the threshold values in modifying the process as needed. The experiments performed on the video sequence of PETS2000 database show that the precision and recall of the proposed approach are higher than other compared approaches.
[1] Yilmaz, A.; Javed, O.; Shah, M., "Object tracking: A survey", Acm computing surveys (CSUR), vol. 38, p. 13 (2006).
[2] Watanabe, G.; Fukui, S.; Iwahori, Y.; Bhuyan, M K.; Woodham, R J.; Adachi, Y.; "Tracking Method in Consideration of Existence of Similar Object around Target Object", Procedia Computer Science, vol. 22, pp. 366-374 (2013).
[3] Cancela, B.; Ortega, M.; Fernandez, A.; Penedo, M G., "Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios", Expert Systems with Applications, vol. 40, pp. 1116-1131 (2013).
[4] Liu, H.; Sun, F., "Efficient visual tracking using particle filter with incremental likelihood calculation", Information Sciences, vol. 195, pp. 141-153 (2012).
[5] Naushad Ali, M. M.; Abdullah-Al-Wadud, M.; Lee, S. L., "Multiple object tracking with partial occlusion handling using salient feature points", Information Sciences, vol. 278, pp. 448-465 (2014).
[6] Harris, C.; Stephens, M., "A combined corner and edge detector", Alvey vision conference , pp. 10-5244 (1988).
[7] Dalal, N.; Triggs, B., "Histograms of oriented gradients for human detection", IEEE, Computer Vision and Pattern Recognition (CVPR). pp. 886-893 (2005).
[8] Xu, R.; Nikouei, S. Y.; Chen, Y.; Polunchenko, A.; Song, S.; Deng,
C.; Faughnan, T. R., "Real-time human objects tracking for smart surveillance at the edge", IEEE, International Conference on Communications (ICC), pp. 1-6 (2018).
[9] Zhou, S.; Ke, M.; Qiu, J.; Wang, J., "A Survey of Multi-object Video Tracking Algorithms", International Conference on Applications and Techniques in Cyber Security and Intelligence. pp. 351-369 (2018).
[10] Xiao, J.; Oussalah, M., "On the use of contextual information for robust colour-based particle filter tracking", Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) , pp. 1-6 (2018).
[11] f. f. p. r. a. u. p. PETS2000/. [12] Sobti, A.; Arora, C.; Balakrishnan, M., "Object detection in real-time systems: Going beyond precision", IEEE. Winter Conference on Applications of Computer Vision (WACV), pp. 1020-1028 (2018).
[13] Li, X.; Wang, K.; Wang, W.; Li, Y., "A multiple object tracking method using Kalman filter", IEEE. International Conference on Information and Automation (ICIA). pp. 1862-1866 (2010).
[14] Comanici, D. u.; Ramesh, V.; Meer, P., "Kernel-based object tracking", IEEE. Transactions on pattern analysis and machine intelligence, vol. 25, pp. 564-577 (2003).
[15] Ning, J.; Zhang, L.; Zhang, D.; Wu, C., "Robust mean-shift tracking with corrected background-weighted histogram", IET computer vision, vol. 6, pp. 62-69 (2012).