International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.14, No.1, Feb. 2022

Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video

Full Text (PDF, 947KB), PP.64-81

Views:23   Downloads:3


Vo Hoai Viet, Huynh Nhat Duy

Index Terms

Object Tracking, Surveillance Systems, Single-Object Tracking, Mean-Shift, Kalman Filter, Particle Filter.


Tracking objects on camera or video is very important for automated surveillance systems. Along with the development of techniques and scientific research in object tracking, automatic surveillance systems have gradually become better. With the input of a frame including the object to be tracked and the location information of the object to be tracked in that video. The output will be the prediction of the position of the object to be tracked on the next frame. This paper presents the comparison and experiment of some traditional object tracking methods and suggestions for improvement between them. Firstly, we examined related studies, traditional object tracking models. Secondly, we examined image and video data sets for verification purposes. Thirdly, experimenting with some related research works in traditional object tracking problems, evaluation of the existing model, what has been achieved and what has not been achieved for the current models. Propose improvements based on the combination of traditional methods. Finally, we aggregate these results to evaluate for each type of object tracking model. The results show that Particles Filter method has the highest CDT with TO score of 0.907971 on VOT dataset and 0.866259 on UAV123 dataset. However, the most stable are the two hybrid methods, the Particle filter base on Mean shift method has a TF score of 31.1 on the VOT dataset and the Kalman Filter base on Mean shift method has a TME score of 28.8233 on the UAV dataset. Because low-level features cannot represent all the information of an object to be tracked during the completion of the experiment, we can conclude that combining deep learning network and using high-level feature into the tracking model can bring better performance in the future. 

Cite This Paper

Vo Hoai Viet, Huynh Nhat Duy, " Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.1, pp. 64-81, 2022.DOI: 10.5815/ijigsp.2022.01.06


[1]D. Comaniciu; P. Meer Mean shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603-619, May 2002, doi: 10.1109/34.1000236.

[2]COMANICIU, D. AND MEER, P. 1999. Mean shift analysis and applications. In IEEE International Conference on Computer Vision (ICCV). Vol. 2. 1197–1203, doi: 10.1109/ICCV.1999.790416.

[3]H. Wang, X. Wang, L. Yu and F. Zhong, "Design of Mean Shift Tracking Algorithm Based on Target Position Prediction," 2019 IEEE International Conference on Mechatronics and Automation (ICMA), 2019, pp. 1114-1119, doi: 10.1109/ICMA.2019.8816295.

[4]Jinya Su; Baibing Li; Wen-Hua Chen (2015). "On existence, optimality and asymptotic stability of the Kalman filter with partially observed inputs". Automatica. 53: 149–154. doi:10.1016/j. automatica.2014.12.044.

[5]Lim Chot Hun, Ong Lee Yeng, Lim Tien Sze and Koo Voon Chet (June 8th, 2016). Kalman Filtering and Its Real‐Time Applications, Real-time Systems, Kuodi Jian, IntechOpen, DOI: 10.5772/62352.

[6]Feng Xiao; Mingyu Song; Xin Guo; Fengxiang Ge. Adaptive Kalman filtering for target tracking. 2016 IEEE/OES China Ocean Acoustics (COA), 2016, pp. 1-5, doi: 10.1109/COA.2016.7535797.

[7]Oğuzhan Gültekİn, Bilge Günsel, "Robust object tracking by variable rate kernel particle filter", 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404479.

[8]Marina A. Zanina, Vitalii A. Pavlov, Sergey V. Zavjalov, Sergey V. Volvenko, "TLD Object Tracking Algorithm Improved with Particle Filter". 2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018, pp. 1-4, doi: 10.1109/TSP.2018.8441515.

[9]Jung Uk Cho; Seung Hun Jin; Xuan Dai Pham; Jae Wook Jeon; Jong Eun Byun; Hoon Kang. A Real-Time Object Tracking System Using a Particle Filter. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, pp. 2822-2827, doi: 10.1109/IROS.2006.282066.

[10]VOT Matej Kristan, Jiri Matas, Aleš Leonardis, Tomáš Vojı́ř, Roman Pflugfelder, Gustavo Fernández, Georg Nebehay, Fatih Porikli and Luka Čehovin, "A Novel Performance Evaluation Methodology for Single-Target Trackers", PAMI, vol. 38, no. 11, pp. 2137-2155, 1 Nov. 2016, doi: 10.1109/TPAMI.2016.2516982.

[11]UAV123 Matthias Mueller, Neil Smith, and Bernard Ghanem, "A Benchmark and Simulator for UAV Tracking", ECCV, 2016. 9905. 445-461. 10.1007/978-3-319-46448-0_27.

[12]C. Cuevas, E. M. Ynez, and N. Garca, Labeled dataset for integral evaluation of moving object detection algorithms: LASIESTA, Computer Vision and Image Understanding, vol. 152, pp. 103-117, 2016.

[13]A. Geiger, P. Lenz and R. Urtasun, "Are we ready for autonomous driving? The KITTI vision benchmark suite," 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3354-3361, doi: 10.1109/CVPR.2012.6248074.

[14]Alper Yilmaz, Omar Javed, Mubarak Shah. Object tracking: A survey. ACM Computing Surveys: Vol 38, No 4, 2006, DOI: 10.1145/1177352.1177355.

[15]Mustansar Fiaz, Arif Mahmood, Sajid Javed, and Soon Ki Jung. 0000. Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends. ACM Comput. Surv. 0, 0, Article 0 (0000), 36 pages.

[16]Vaswani, N.; Rathi, Y.; Yezzi, A.; Tannenbaum, A. (2007). "Tracking deforming objects using particle filtering for geometric active contours". IEEE Transactions on Pattern Analysis and Machine Intelligence. 29 (8): 1470–1475, Aug. 2007, doi: 10.1109/TPAMI.2007.1081.

[17]L. M. Brown, A. W. Senior, Ying-li Tian, Jonathan Connell, Arun Hampapur, Chiao-Fe Shu, Hans Merkl, Max Lu, “Performance Evaluation of Surveillance Systems Under Varying Conditions”, IEEE Int'l Workshop on Performance Evaluation of Tracking and Surveillance, Colorado, Jan 2005.

[18]F. Bashir, F. Porikli. “Performance evaluation of object detection and tracking systems”, IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), June 2006.

[19]Sven Ubik; Jiří Pospíšilík. Video Camera Latency Analysis and Measurement. IEEE Transactions on Circuits and Systems for Video Technology (Volume: 31, Issue: 1, Jan. 2021): 140 - 147. DOI: 10.1109/TCSVT.2020.2978057.

[20]T. Ellis, “Performance Metrics and Methods for Tracking in Surveillance”, Third IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, June, Copenhagen, Denmark, 2002, pp26-31.

[21]J. Nascimento, J. Marques, “Performance evaluation of object detection algorithms for video surveillance”, IEEE Transactions on Multimedia, vol. 8, no. 4, pp. 761-774, Aug. 2006, doi: 10.1109/TMM.2006.876287.

[22]N. Lazarevic - McManus, J.R. Renno, D. Makris, G.A. Jones, “An Object-based Comparative Methodology for Motion Detection based on the F-Measure”, in 'Computer Vision and Image Understanding', Special Issue on Intelligent Visual Surveillance TO APPEAR, 2007, Volume 111, Issue 1, 2008, Pages 74-85, ISSN 1077-3142, 10.1016/j.cviu.2007.07.007.

[23]C.J. Needham, R.D. Boyle. “Performance Evaluation Metrics and Statistics for Positional Tracker Evaluation” International Conference on Computer Vision Systems (ICVS'03), Graz, Austria, April 2003, pp. 278 – 289. 278-289. 10.1007/3-540-36592-3_27.

[24]J. F. Henriques, R. Caseiro, P. Martins and J. Batista, "High-speed tracking with kernelized correlation filters", IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, pp. 583-596, Mar. 2015, doi: 10.1109/TPAMI.2014.2345390. 

[25]Z. Soleimanitaleb, M. A. Keyvanrad and A. Jafari, "Object Tracking Methods: A Review," 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 282-288, doi: 10.1109/ICCKE48569.2019.8964761.

[26]Y. Ivanov et al., "Adaptive moving object segmentation algorithms in cluttered environments," The Experience of Designing and Application of CAD Systems in Microelectronics, 2015, pp. 97-99, doi: 10.1109/CADSM.2015.7230806.

[27]K. R. Reddy, K. H. Priya and N. Neelima, "Object Detection and Tracking -- A Survey," 2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 418-421, doi: 10.1109/CICN.2015.317.

[28]Hamed Tirandaz, Sassan Azadi,"Utilizing GVF Active Contours for Real-Time Object Tracking", IJIGSP, vol.7, no.6, pp. 59-65, 2015.DOI: 10.5815/ijigsp.2015.06.08.

[29]Haocheng Le, Linglong Hu, Yuanjing Feng,"Momentum Based Level Set Method For Accurate Object Tracking", International Journal of Intelligent Systems and Applications (IJISA), vol.2, no.2, pp.10-16, 2010. DOI: 10.5815/ijisa.2010.02.02.

[30]Adithya Urs, Nagaraju C, "Object Motion Direction Detection and Tracking for Automatic Video Surveillance", International Journal of Education and Management Engineering (IJEME), Vol.11, No.2, pp. 32-39, 2021. DOI: 10.5815/ijeme.2021.02.04.

[31]Muhammad Tayyab, Muhammad Tahir Qadri, Raheel Ahmed, Maryam Ahmad Dhool,"Real Time Object Tracking using FPGA Development Kit", International Journal of Information Technology and Computer Science (IJITCS), vol.6, no.11, pp.54-58, 2014. DOI: 10.5815/ijitcs.2014.11.08.

[32]G. Mallikarjuna Rao, Ch. Satyanarayana,"Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching", International Journal of Intelligent Systems and Applications (IJISA), vol.6, no.5, pp.83-89, 2014. DOI: 10.5815/ijisa.2014.05.09.

[33]Ravi Kumar Jatoth, Sampad Shubhra, Ejaz Ali,"Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking", IJIEEB, vol.5, no.5, pp.17-24, 2013. DOI: 10.5815/ijieeb.2013.05.03.