In this paper, a machine learning-based object detection and tracking approach in radar system is proposed via using the range-angle map as the input. Specifically, by using the You Only Look Once (YOLO) for object detection and Deep Simple Online and Realtime Tracking (D-SORT) for tracking, the proposed approach can improve the detection and tracking performance, reducing the parameters needed to be manually selected, and providing more relevant information, such as the shape, size, and category of the object. We conduct the realistic simulations to evaluate the proposed approach. Results show that our proposed approach can outperform the conventional radar processing approach in terms of detection and tracking performance. Furthermore, results indicate that the object categorization of the proposed approach is accurate.


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    Title :

    Deep-Learning Based Multi-Object Detection and Tracking using Range-Angle Map in Automotive Radar Systems


    Contributors:
    Kim, Ji-He (author) / Lee, Ming-Chun (author) / Lee, Ta-Sung (author)


    Publication date :

    2022-06-01


    Size :

    3673811 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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