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.

scholarly journals A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

2018 ◽  
Vol 10 (9) ◽  
pp. 1347 ◽  
Author(s):  
Ting Chen ◽  
Andrea Pennisi ◽  
Zhi Li ◽  
Yanning Zhang ◽  
Hichem Sahli
Keyword(s):  
Object Tracking ◽  
Target Tracking ◽  
Moving Objects ◽  
State Of The Art ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Challenging Problem ◽  
Compressive Tracking ◽  
Vehicle Motion ◽  
Airborne Vehicle

Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods.

Tracking Multiple Objects Is Limited Only by Object Spacing, Not by Speed, Time, or Capacity

2010 ◽  
Vol 21 (7) ◽  
pp. 920-925 ◽  
Author(s):  
S.L. Franconeri ◽  
S.V. Jonathan ◽  
J.M. Scimeca
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Multiple Object Tracking ◽  
Single Object ◽  
Multiple Objects ◽  
Performance Constraints ◽  
Root Cause ◽  
Multiple Object ◽  
Tracking Time ◽  
Environment Performance

In dealing with a dynamic world, people have the ability to maintain selective attention on a subset of moving objects in the environment. Performance in such multiple-object tracking is limited by three primary factors—the number of objects that one can track, the speed at which one can track them, and how close together they can be. We argue that this last limit, of object spacing, is the root cause of all performance constraints in multiple-object tracking. In two experiments, we found that as long as the distribution of object spacing is held constant, tracking performance is unaffected by large changes in object speed and tracking time. These results suggest that barring object-spacing constraints, people could reliably track an unlimited number of objects as fast as they could track a single object.

scholarly journals A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

2018 ◽  
Author(s):  
Ting Chen ◽  
Andrea Pennisi ◽  
Zhi Li ◽  
Yanning Zhang ◽  
Hichem Sahli
Keyword(s):  
Object Tracking ◽  
Target Tracking ◽  
Moving Objects ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Challenging Problem ◽  
Compressive Tracking ◽  
Vehicle Motion ◽  
Airborne Vehicle ◽  
State Of Art

Multi-object tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, size, appearance and motion of the moving objects as well as occlusions due to the interaction between the moving objects and with other static objects in the scene.To deal with these problems, this work proposes a four-stage Hierarchical Association framework for multiple object Tracking in Airborne video (HATA). The proposed framework combines data association-based tracking (DAT) methods and target tracking using a Compressive Tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne video datasets show significant tracking improvement compared to existing state-of-art methods.

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...

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