{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:32:34Z","timestamp":1769574754724,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T00:00:00Z","timestamp":1618617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC\u2019s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.<\/jats:p>","DOI":"10.3390\/s21082841","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T21:59:49Z","timestamp":1618869589000},"page":"2841","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4278-6166","authenticated-orcid":false,"given":"Khizer","family":"Mehmood","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Abdul","family":"Jalil","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Ahmad","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Bahria University, Islamabad 44000, Pakistan"}]},{"given":"Baber","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9633-9927","authenticated-orcid":false,"given":"Maria","family":"Murad","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Khalid Mehmood","family":"Cheema","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Southeast University, Nanjing 210096, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1926-9486","authenticated-orcid":false,"given":"Ahmad H.","family":"Milyani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Spatio-temporal information for human action recognition","volume":"39","author":"Yao","year":"2016","journal-title":"Eurasip J. Image Video Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, X., Chen, D., Yang, T., Hu, B., and Zhang, J. (2016, January 19\u201321). Action recognition based on object tracking and dense trajectories. Proceedings of the International Conference on Automatica (ICA-ACCA), Curico, Chile.","DOI":"10.1109\/ICA-ACCA.2016.7778391"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.patrec.2014.04.011","article-title":"Human activity recognition from 3d data: A review","volume":"48","author":"Aggarwal","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_4","unstructured":"Hui, Z., Yaohua, X., Lu, M., and Jiansheng, F. (July, January 29). Vision-based real-time traffic accident detection. Proceedings of the 11th World Congress on Intelligent Control and Automation (WCICA), Shenyang, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tian, B., Yao, Q., Gu, Y., Wang, K., and Li, Y. (2011, January 5\u20137). Video processing techniques for traffic flow monitoring: A survey. Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA.","DOI":"10.1109\/ITSC.2011.6083125"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.future.2020.04.014","article-title":"Research on the improvement of vision target tracking algorithm for Internet of things technology and Simple extended application in pellet ore phase","volume":"110","author":"Li","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, Z., Zhang, L., Yang, Y., Kang, Q., and Sun, D. (2019). Object Tracking for a Smart City Using IoT and Edge Computing. Sensors, 19.","DOI":"10.3390\/s19091987"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gong, X., Le, Z., Wang, H., and Wu, Y. (2020). Study on the Moving Target Tracking Based on Vision DSP. Sensors, 20.","DOI":"10.3390\/s20226494"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Oh, S.H., Javed, S., and Jung, S.K. (2013, January 16\u201318). Foreground Object Detection and Tracking for Visual Surveillance System: A Hybrid Approach. Proceedings of the 11th International Conference on Frontiers of Information Technology, Islamabad, Pakistan.","DOI":"10.1109\/FIT.2013.10"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Staniszewski, M., Foszner, P., Kostorz, K., Michalczuk, A., Wereszczy\u0144ski, K., Cogiel, M., Golba, D., Wojciechowski, K., and Pola\u0144ski, A. (2020). Application of Crowd Simulations in the Evaluation of Tracking Algorithms. Sensors, 20.","DOI":"10.3390\/s20174960"},{"key":"ref_11","unstructured":"Ali, A., Kausar, H., and Muhammad, I.K. (2009, January 19\u201322). Automatic visual tracking and firing system for anti-aircraft machine gun. Proceedings of the 6th International Bhurban Conference on Applied Sciences & Technology (IBCAST), Islamabad, Pakistan."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.jvoice.2010.05.002","article-title":"Towards the automatic study of the vocal tract from magnetic resonance images","volume":"25","author":"Vasconcelos","year":"2011","journal-title":"J. Voice Off. J. Voice Found."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1166\/jmihi.2017.2139","article-title":"Automatic fovea center localization in retinal images using saliency-guided object discovery and feature extraction","volume":"7","author":"Zhou","year":"2017","journal-title":"J. Med. Imaging Health Inf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11704-015-4246-3","article-title":"Visual object tracking\u2014classical and contemporary approaches","volume":"10","author":"Ali","year":"2016","journal-title":"Front. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yoon, G.-J., Hwang, H.J., and Yoon, S.M. (2018). Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning. Sensors, 18.","DOI":"10.3390\/s18103513"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3309665","article-title":"Handcrafted and deep trackers: Recent visual object tracking approaches and trends","volume":"52","author":"Fiaz","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TPAMI.2011.66","article-title":"Robust Visual Tracking and Vehicle Classification via Sparse Representation","volume":"33","author":"Mei","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hare, S., Saffari, A., and Torr, P.H.S. (2011, January 6\u201313). Struck: Structured output tracking with kernels. Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126251"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhang, L., Liu, Q., Zhang, D., and Yang, M.H. (2014, January 6\u20137). Fast visual tracking via dense spatio-temporal context learning. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_9"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/13682199.2019.1567020","article-title":"Adaptive spatio-temporal context learning for visual tracking","volume":"67","author":"Zhang","year":"2019","journal-title":"Imaging Sci. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11042-017-5533-9","article-title":"Online convolution network tracking via spatio-temporal context","volume":"78","author":"Wang","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","unstructured":"Wan, H., Li, W., and Ye, G. (June, January 31). An improved spatio-temporal context tracking algorithm. Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), Wuhan, China."},{"key":"ref_23","first-page":"1","article-title":"An improved spatio-temporal context tracking algorithm based on scale correlation filter","volume":"11","author":"Li","year":"2019","journal-title":"Adv. Mech. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11554-012-0251-z","article-title":"Stabilized active camera tracking system","volume":"11","author":"Ahmed","year":"2016","journal-title":"J. Real-Time Image Proc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2017, January 21\u201326). Context-Aware Correlation Filter Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.152"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ali, A., Jalil, A., and Ahmed, J. (2016, January 21\u201322). A new template updating method for correlation tracking. Proceedings of the International Conference on Image and Vision Computing (IVCNZ), Palmerston North, New Zealand.","DOI":"10.1109\/IVCNZ.2016.7804462"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shin, J., Kim, H., Kim, D., and Paik, J. (2020). Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter. Appl. Sci., 10.","DOI":"10.3390\/app10020713"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2014, January 9). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","article-title":"Discriminative Scale Space Tracking","volume":"39","author":"Danelljan","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhou, W., Shi, L., and Li, D. (2018). Motion-Aware Correlation Filters for Online Visual Tracking. Sensors, 18.","DOI":"10.3390\/s18113937"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/LSP.2019.2963147","article-title":"FAST: Fast and Accurate Scale Estimation for Tracking","volume":"27","author":"Ma","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, J. (2014, January 6\u20137). A scale adaptive kernel correlation filter tracker with feature integration. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"ref_34","unstructured":"Panqiao, C., and Mengzhao, Y. (2016, January 23\u201324). STC Tracking Algorithm Based on Kalman Filter. Proceedings of the 4th International Conference on Machinery, Materials and Computing Technology, Hangzhou, China."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Munir, F., Minhas, F., Jalil, A., and Jeon, M. (2017, January 1\u20133). Real time eye tracking using Kalman extended spatio-temporal context learning. Proceedings of the Second International Workshop on Pattern Recognition, Singapore.","DOI":"10.1117\/12.2280271"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.3390\/s21041129","article-title":"Learning Local\u2013Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection","volume":"21","author":"Zhang","year":"2021","journal-title":"Sensors"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103596","DOI":"10.1016\/j.robot.2020.103596","article-title":"Vehicle tracking with Kalman filter using online situation assessment","volume":"131","author":"Khalkhali","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1007\/s11760-014-0612-0","article-title":"Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking","volume":"9","author":"Ali","year":"2015","journal-title":"Signal Image Video Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, J., Miao, Y., Yang, Y., Zhao, Z., Wang, Z., Sun, Q., and Wu, D.O. (2019). Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking. Mathematicsc, 7.","DOI":"10.3390\/math7111059"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mehmood, K., Jalil, A., Ali, A., Khan, B., Murad, M., Khan, W.U., and He, Y. (2021). Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking. Electronics, 10.","DOI":"10.3390\/electronics10010043"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"149077","DOI":"10.1109\/ACCESS.2020.3015580","article-title":"AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access."},{"key":"ref_42","unstructured":"Zekavat, R., and Buehrer, R.M. (2018). An Introduction to Kalman Filtering Implementation for Localization and Tracking Applications. Handbook of Position Location: Theory, Practice, and Advances, Wiley Online Library. [2nd ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, M., Liu, Y., and Huang, Z. (2017, January 21\u201326). Large Margin Object Tracking with Circulant Feature Maps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.510"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5630","DOI":"10.1109\/TIP.2015.2482905","article-title":"Encoding color information for visual tracking: Algorithms and benchmark","volume":"24","author":"Liang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.H. (2013, January 23\u201328). Online object tracking: A benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object tracking benchmark","volume":"37","author":"Wu","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). A Benchmark and Simulator for UAV Tracking. Computer Vision\u2014ECCV 2016. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-46484-8"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2841\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:15Z","timestamp":1760161755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2841"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,17]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082841"],"URL":"https:\/\/doi.org\/10.3390\/s21082841","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,17]]}}}