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Open AccessArticle

A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems

by Zhongli Wang 1,2,*, Litong Fan 1,3 and Baigen Cai 1,2
1
School of Electronic Information and Engineering, Beijing Jiaotong University, Beijing 100044, China
2
Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China
3
School of Computer Information Management, Inner Mongolia University of Finance and Economics, Hohot 010010, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2363; https://doi.org/10.3390/s18072363
Received: 7 May 2018 / Revised: 8 July 2018 / Accepted: 15 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
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’s 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. View Full-Text
Keywords: tracking by detection; 3D relative-motion model; sequential Bayesian framework; multi-object tracking tracking by detection; 3D relative-motion model; sequential Bayesian framework; multi-object tracking
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Wang, Z.; Fan, L.; Cai, B. A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems. Sensors 2018, 18, 2363.

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