Six Object Tracking Algorithms: A Comparative Study

Abstract Compared with 2D multi-object tracking algorithms, 3D multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.

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Comparative study of maximum power point tracking algorithms using an experimental, programmable, maximum power point tracking test bed

Conference Record of the Twenty-Eighth IEEE Photovoltaic Specialists Conference - 2000 (Cat. No.00CH37036) ◽

10.1109/pvsc.2000.916230 ◽

2002 ◽

Cited By ~ 232

Author(s):

D.P. Hohm ◽

M.E. Ropp

Keyword(s):

Comparative Study ◽

Maximum Power Point Tracking ◽

Maximum Power ◽

Test Bed ◽

Maximum Power Point ◽

Point Tracking ◽

Power Point Tracking ◽

Tracking Algorithms ◽

Power Point

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Enhancing the Performance of a Rainfall Measurement System Using Artificial Neural Networks Based Object Tracking Algorithms

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) ◽

10.1109/i2mtc.2019.8827108 ◽

2019 ◽

Author(s):

Chih-Yen Chen ◽

Lijuan Wang ◽

Chi-Hung Hwang ◽

Chi-Wen Hsieh ◽

Po-Wei Chi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Object Tracking ◽

Measurement System ◽

Tracking Algorithms ◽

Artificial Neural

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Comparison of Tracking Techniques on 360-Degree Videos

Applied Sciences ◽

10.3390/app9163336 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3336 ◽

Cited By ~ 4

Author(s):

Tzu-Wei Mi ◽

Mau-Tsuen Yang

Keyword(s):

Augmented Reality ◽

Object Tracking ◽

Ground Truth ◽

Physical Object ◽

Future Research ◽

Scale Change ◽

Tracking Algorithms ◽

Viewpoint Change ◽

Pros And Cons

With the availability of 360-degree cameras, 360-degree videos have become popular recently. To attach a virtual tag on a physical object in 360-degree videos for augmented reality applications, automatic object tracking is required so the virtual tag can follow its corresponding physical object in 360-degree videos. Relative to ordinary videos, 360-degree videos in an equirectangular format have special characteristics such as viewpoint change, occlusion, deformation, lighting change, scale change, and camera shakiness. Tracking algorithms designed for ordinary videos may not work well on 360-degree videos. Therefore, we thoroughly evaluate the performance of eight modern trackers in terms of accuracy and speed on 360-degree videos. The pros and cons of these trackers on 360-degree videos are discussed. Possible improvements to adapt these trackers to 360-degree videos are also suggested. Finally, we provide a dataset containing nine 360-degree videos with ground truth of target positions as a benchmark for future research.

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