Multi-resolution Rescored ByteTrack for Video Object Detection on
  Ultra-low-power Embedded Systems
Luca Bompani Manuele Rusci Daniele Palossi Francesco Conti Luca Benini
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