Microscopic Traffic Parameters Estimation from UAV Video Using Multiple Object Tracking of Deep Learning-based

2021 ◽  
Vol 20 (5) ◽  
pp. 83-99
Author(s):  
Bokyung Jung ◽  
◽  
Sunghyuk Seo ◽  
Boogi Park ◽  
Sanghoon Bae
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Parameters Estimation ◽  
Multiple Object

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

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 Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking

2019 ◽  
Vol 181 (1) ◽  
pp. 28-42 ◽  
Author(s):  
Jonathon A. Gibbs ◽  
Alexandra J. Burgess ◽  
Michael P. Pound ◽  
Tony P. Pridmore ◽  
Erik H. Murchie
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object

scholarly journals Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning

2022 ◽  
Author(s):  
Madison Harasyn ◽  
Wayne S. Chan ◽  
Emma L. Ausen ◽  
David G. Barber
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Marine Mammal ◽  
Multiple Object Tracking ◽  
Learning Technology ◽  
Tracking Accuracy ◽  
Aerial Photos ◽  
Detection And Tracking ◽  
Multiple Object ◽  
Computer Based

Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collected from a stationary tethered system. Automated computer-based object detection achieved the following precision and recall, respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and kayak = 96%/96%. We then tested the performance of computer vision tracking of belugas and manned watercraft in drone videos using the DeepSORT tracking algorithm, which achieved a multiple-object tracking accuracy (MOTA) ranging from 37% – 88% and multiple object tracking precision (MOTP) between 63% – 86%. Results from this research indicate that deep learning technology can detect and track features more consistently than human annotators, allowing for larger datasets to be processed within a fraction of the time while avoiding discrepancies introduced by labeling fatigue or multiple human annotators.

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