Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
This paper introduces Multi-Resolution Rescored ByteTrack (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 2.25x by alternating the processing of high-resolution images (320x320 pixels) with multiple down-sized frames (192x192 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
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Publication status
published
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Editor
Book title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Journal / series
Volume
Pages / Article No.
2182 - 2190
Publisher
IEEE
Event
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2024)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Video object detection; Neural networks; Ultra-low-power; Embedded systems
Organisational unit
03996 - Benini, Luca / Benini, Luca
Notes
Funding
207913 - TinyTrainer: On-chip Training for TinyML devices (SNF)