Abstract
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.
Highlights
Multiple object tracking on a moving platform is an active research topic in computer vision, with applications such as automotive driver assistance systems, robot navigation and traffic safety
Traditional tracking-by-detection methods are heavily dependent on detection results, because there are many factors, such as inaccurate detections, similarity between objects, frequent occlusion, etc., which lead to tracklets or tracking drift, making multi-object tracking (MOT) still a very challenging task
This work represents the motion patterns of the targets with a simple linear dynamical system (LDS), and a training process is necessary for the object class assignment; it limits the applicability of the method
Summary
Multiple object tracking on a moving platform is an active research topic in computer vision, with applications such as automotive driver assistance systems, robot navigation and traffic safety. The global association is realized by a sliding window or hierarchical association, and often formulated as a graph optimization problem It models the tracking problem as a network flow and solves it either with k-shortest paths in dynamic programing with non-maximum suppression (DP NMS) [1], as a Generalized Maximum Multi-Clique problem which is solved with Binary Integer Programing in reference [2], in a conditional random field (as in SegTrack) [3], with Long-term time-sensitive costs for CRF(LTTSC-CRF) [4], or with discrete-continuous energy minimization [5], to name a few.
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