{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:24:24Z","timestamp":1775593464679,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"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>Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.<\/jats:p>","DOI":"10.3390\/s21248481","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"8481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Efficient Online Object Tracking Scheme for Challenging Scenarios"],"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":"Ahmad","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Bahria University, Islamabad 44000, Pakistan"}]},{"given":"Abdul","family":"Jalil","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"given":"Baber","family":"Khan","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\/0000-0002-9633-9927","authenticated-orcid":false,"given":"Maria","family":"Murad","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1926-9486","authenticated-orcid":false,"given":"Ahmad H.","family":"Milyani","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/j.patrec.2010.04.017","article-title":"Multiple and variable target visual tracking for video-surveillance applications","volume":"31","author":"Pantrigo","year":"2010","journal-title":"Pattern Recognit. 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