LiteReconfig: An efficient and adaptive video object detection framework for mobile GPUs

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Track Code	2022-CHAT-69774
Categories	Computer Technology
Keywords	Autonomous vehicles Computer Technology Decision Making mobile computing object detection Self-Driving Cars Software
Public Abstract	Researchers at Purdue University have developed an adaptive video object detection system (LiteReconfig) intended for systems where there are limited computing resources and low latency requirements. These challenges are particularly relevant on devices using mobile hardware to analyze video streams. LiteReconfig uses a cost-benefit analyzer to schedule switching to new image features as video conditions change, allowing for a more optimal balance of accuracy and computation time. This is done in a content-aware manner, resulting in selections that are tailored to the specific content of the video stream. This system is intended to run on mobile devices and has shown significantly better performance when compared to existing adaptive object detection systems, while running on an NVIDIA AGX Xavier at speeds of up to 50 fps. This technology has applications in the autonomous driving, machine vision, and facial recognition sectors as it allows for improved object detection while minimizing the computational requirements and latency. Advantages: - Low latency capability (50 fps) - Mobile GPU compatible Applications: - Mobile computing - Autonomous vehicles - Real time object/facial detection Technology Validation: This technology has been validated using the ImageNet VID 2015 benchmark. The model achieved 1.8% to 3.5% greater object detection accuracy and 20.3X to 74.9X lower latency when compared to existing state of the art systems. Related Publications: Ran Xu, Jayoung Lee, Pengcheng Wang, Saurabh Bagchi, Yin Li, and Somali Chaterji. 2022. LiteReconfig: cost and content aware reconfiguration of video object detection systems for mobile GPUs. In Proceedings of the Seventeenth European Conference on Computer Systems (EuroSys '22). Association for Computing Machinery, New York, NY, USA, 334–351. https://doi.org/10.1145/3492321.3519577
People	Chaterji, Somali (Project leader) Bagchi, Saurabh Xu, Ran
Intellectual Property	Application Date Mar 10, 2023 Type Utility-Gov. Funding Country of Filing United States Patent Number (None) Issue Date (None) Application Date Mar 10, 2022 Type Copyright Country of Filing United States Patent Number (None) Issue Date (None) Application Date Mar 10, 2022 Type Provisional-Patent Country of Filing United States Patent Number (None) Issue Date (None)
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