# Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems Source: [Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:36:44.860Z Authors: Luca Bompani, Manuele Rusci, Daniele Palossi, Francesco Conti, Luca Benini Published: 2024-04-17T15:45:49.000Z ## Abstract 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()