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

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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Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation

Symmetry ◽

10.3390/sym13020266 ◽

2021 ◽

Vol 13 (2) ◽

pp. 266 ◽

Cited By ~ 1

Author(s):

Yifeng Wang ◽

Zhijiang Zhang ◽

Ning Zhang ◽

Dan Zeng

Keyword(s):

Object Tracking ◽

Multiple Object Tracking ◽

Tracking Accuracy ◽

Feature Maps ◽

Backbone Networks ◽

Multiple Object ◽

The Arts ◽

Training Stage ◽

The One ◽

Baseline State

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).

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Real-time multiple object tracking using deep learning methods

Neural Computing and Applications ◽

10.1007/s00521-021-06391-y ◽

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

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Deep learning for multiple object tracking: a survey

IET Computer Vision ◽

10.1049/iet-cvi.2018.5598 ◽

2019 ◽

Vol 13 (4) ◽

pp. 355-368 ◽

Cited By ~ 7

Author(s):

Yingkun Xu ◽

Xiaolong Zhou ◽

Shengyong Chen ◽

Fenfen Li

Keyword(s):

Deep Learning ◽

Object Tracking ◽

Multiple Object Tracking ◽

Multiple Object

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Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

The Scientific World JOURNAL ◽

10.1155/2014/481719 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Zhenghao Xi ◽

Heping Liu ◽

Huaping Liu ◽

Bin Yang

Keyword(s):

Integer Programming ◽

Object Tracking ◽

Programming Problem ◽

Shortest Path ◽

Multiple Object Tracking ◽

Global Optimum ◽

Integer Programming Problem ◽

Tracking Accuracy ◽

Worst Case ◽

Multiple Object

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

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Multiple Object Tracking in Deep Learning Approaches: A Survey

Electronics ◽

10.3390/electronics10192406 ◽

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.

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