Coupled data association andl1 minimization for multiple object tracking under occlusion

2014 ◽  
Author(s):  
Xue Wang ◽  
Qing Wang
Keyword(s):  
Object Tracking ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Multiple Object

Data association in multiple object tracking: A survey of recent techniques

2021 ◽  
pp. 116300
Author(s):  
Lionel Rakai ◽  
Huansheng Song ◽  
Shijie Sun ◽  
Wentao Zhang ◽  
Yanni Yang
Keyword(s):  
Object Tracking ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Multiple Object

Higher-Order Multiple Object Tracking

2021 ◽  
Author(s):  
Roberto D. Henschel
Keyword(s):  
Object Tracking ◽  
Optimization Problems ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Higher Order ◽  
Path Model ◽  
Quadratic Program ◽  
Multiple Object ◽  
The Individual ◽  
Available Information

This dissertation deals with camera-based offline multiple object tracking and explores higher-order data association models. Due to their extensive exploitation of the available information, such models are promising approaches in current research. However, they commonly represent NP-hard optimization problems so that their application in practice is challenging. The first part of this thesis proposes a binary quadratic program that enables to globally fuse signals within a higher-order data association model. This enables to overcome weaknesses of the individual signals. An approximate solver based on the Frank-Wolfe algorithm is presented and analyzed. Its benefit is demonstrated in two setups: fusion of two detectors and combining signals coming from a video and body-worn inertial measurement units. The second part of this thesis proposes an extension of the disjoint path model by higher-order information and connectivity priors, resulting in a binary linear program. Efficient separation algorithms are proposed and integrated into a cutting-plane algorithm, making it possible for the first time to solve higher-order data association globally in practice. Contents 1 Intro...

An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association

2013 ◽  
Vol 427-429 ◽  
pp. 1822-1825
Author(s):  
Zhen Hai Wang ◽  
Ki Cheon Hong
Keyword(s):  
Object Tracking ◽  
Energy Minimization ◽  
Moving Objects ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Object Extraction ◽  
Frame Difference ◽  
Multiple Object ◽  
Important Research Topic ◽  
Key Steps

multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.

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