Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Deep Learning for Autonomous Vehicle Object Tracking and Recognition

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  • Dr. Xiaobo Li
Dr. Xiaobo Li
Associate Professor of Electrical Engineering, Tsinghua University, China
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Published 14-05-2023

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Xiaobo Li, “Deep Learning for Autonomous Vehicle Object Tracking and Recognition”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 375–403, May 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/83

Abstract

Current tracking systems for autonomous vehicles are mostly reliant on object detection with detection-based tracking (DBT) (Li et al., 2016). However, to successfully track objects in an image series, these systems typically require an object detection step in the first frame, which could be challenging in cases of highly dynamic backgrounds or difficult object orientations. Additionally, they are susceptible to noise with sensor actors (e.g., pedestrians) after detection, failing to incorporate new target detections when detecting signals, and losing sight of targets (e.g., occlusions). The related task of visual multi-object tracking (V MOT) seeks to address these problems, yet it is also prone to significant issues, such as detection drifting, new object detections, as well as object occlusion and dynamic scenarios. As such, more robust algorithms that work well in different real-world dynamics and camera setups are required in the application of autonomous vehicles.[2]Autonomous vehicles rely on key perception capabilities including object detection, tracking and recognition to operate safely in the traffic ecosystem.

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