Real-time multiple object tracking using deep learning methods

2021 ◽  
Author(s):  
Dimitrios Meimetis ◽  
Ioannis Daramouskas ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Learning Methods ◽  
Multiple Object

scholarly journals Real-Time Multiple Object Tracking Using Virtual Shells

2013 ◽  
Vol 18 (3) ◽  
pp. 373-382
Author(s):  
Taygun Kekec ◽  
Mustafa Unel ◽  
Hakan Erdogan
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Multiple Object

Deep learning for multiple object tracking: a survey

2019 ◽  
Vol 13 (4) ◽  
pp. 355-368 ◽  
Author(s):  
Yingkun Xu ◽  
Xiaolong Zhou ◽  
Shengyong Chen ◽  
Fenfen Li
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object

scholarly journals Multiple Object Tracking in Deep Learning Approaches: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2406
Author(s):  
Yesul Park ◽  
L. Minh Dang ◽  
Sujin Lee ◽  
Dongil Han ◽  
Hyeonjoon Moon
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Motion Trajectory ◽  
Learning Approaches ◽  
Multiple Object ◽  
Problems And Solutions ◽  
Benchmark Datasets ◽  
Future Work ◽  
Appearance Changes

Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

scholarly journals A Region-Based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking

2019 ◽  
Vol 41 (8) ◽  
pp. 1797-1812 ◽  
Author(s):  
Henning Tjaden ◽  
Ulrich Schwanecke ◽  
Elmar Schomer ◽  
Daniel Cremers
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Multiple Object Tracking ◽  
Multiple Object
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