{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:46:34Z","timestamp":1771699594783,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T00:00:00Z","timestamp":1532044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61573057"],"award-info":[{"award-number":["61573057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds of BJTU","award":["2017JBZ002"],"award-info":[{"award-number":["2017JBZ002"]}]},{"name":"the key technology research and equipment development of medium speed maglev operation control","award":["2016YFB1200601-B24"],"award-info":[{"award-number":["2016YFB1200601-B24"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relative-motion model into a sequential Bayesian framework to track multiple objects and the camera\u2019s ego-motion estimation. A 3D relative-motion model that describes spatial relations among objects is exploited for predicting object states robustly and recovering objects when occlusion and mis-detections occur. Reversible jump Markov chain Monte Carlo (RJMCMC) particle filtering is applied to solve the posteriori estimation problem. Both quantitative and qualitative experiments with benchmark datasets and video collected on campus were conducted, which confirms that the proposed method is outperformed in many evaluation metrics.<\/jats:p>","DOI":"10.3390\/s18072363","type":"journal-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T03:24:27Z","timestamp":1532316267000},"page":"2363","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems"],"prefix":"10.3390","volume":"18","author":[{"given":"Zhongli","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic Information and Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Litong","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohot 010010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baigen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pirsiavash, H., Ramanan, D., and Fowlkes, C.C. 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