Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
<|reference_start|>Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems: This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces the average compute load of an off-the-shelf Deep Neural Network (DNN) based object detector by up to 2.25$\times$ by alternating the processing of high-resolution images (320$\times$320 pixels) with multiple down-sized frames (192$\times$192 pixels). To tackle the accuracy degradation due to the reduced image input size, MR2-ByteTrack correlates the output detections over time using the ByteTrack tracker and corrects potential misclassification using a novel probabilistic Rescore algorithm. By interleaving two down-sized images for every high-resolution one as the input of different state-of-the-art DNN object detectors with our MR2-ByteTrack, we demonstrate an average accuracy increase of 2.16% and a latency reduction of 43% on the GAP9 microcontroller compared to a baseline frame-by-frame inference scheme using exclusively full-resolution images. Code available at: https://github.com/Bomps4/Multi_Resolution_Rescored_ByteTrack<|reference_end|>
@article{bompani2024multi-resolution,
    title={Multi-resolution Rescored ByteTrack for Video Object Detection on
  Ultra-low-power Embedded Systems},
    author={Luca Bompani, Manuele Rusci, Daniele Palossi, Francesco Conti and Luca
  Benini},
    journal={arXiv preprint arXiv:2404.11488},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11488},
    primaryClass={cs.CV cs.AI}
}