Transformer-based two-source motion model for multi-object tracking

2022 ◽  
Author(s):  
Jieming Yang ◽  
Hongwei Ge ◽  
Shuzhi Su ◽  
Guoqing Liu
Keyword(s):  
Object Tracking ◽  
Motion Model ◽  
Source Motion

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

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2363 ◽  
Author(s):  
Zhongli Wang ◽  
Litong Fan ◽  
Baigen Cai
Keyword(s):  
Motion Estimation ◽  
Object Tracking ◽  
Relative Motion ◽  
Particle Filtering ◽  
Spatial Relations ◽  
Visual Object ◽  
Camera Motion ◽  
Motion Model ◽  
Unified Framework ◽  
Map Solution

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.

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

The Number & Quality of Tracked Representations in Multi-Object Tracking

2012 ◽  
Author(s):  
Weiwei Zhang ◽  
Andrew P. Yonelinas
Keyword(s):  
Object Tracking

Predictable paths increase the use of motion in object tracking

2010 ◽  
Author(s):  
Adriane E. Seiffert ◽  
Rebecca St. Clair
Keyword(s):  
Object Tracking

Location and identity are entangled in multiple-object tracking

2010 ◽  
Author(s):  
Todd S. Horowitz ◽  
Michael A. Cohen ◽  
Yair Pinto ◽  
Piers D. L. Howe
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object
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