research-article

ApproxDet: content and contention-aware approximate object detection for mobiles

Authors:

Chen-lin Zhang,

Pengcheng Wang,

Somali Chaterji,

Saurabh BagchiAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 449 - 462

https://doi.org/10.1145/3384419.3431159

Published: 16 November 2020 Publication History

Abstract

Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous systems. With an evolution of heterogeneous client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices for low latency and real-time processing and to preserve user privacy. However, most video analytic systems are heavyweight and are trained offline with some pre-defined latency or accuracy requirements. This makes them unable to adapt at runtime in the face of three types of dynamism --- the input video characteristics change, the amount of compute resources available on the node changes due to co-located applications, and the user's latency-accuracy requirements change. In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios. To achieve this, we introduce a multi-branch object detection kernel, which incorporates a data-driven modeling approach on the performance metrics, and a latency SLA-driven scheduler to pick the best execution branch at runtime. We evaluate ApproxDet on a large benchmark video dataset and compare quantitatively to AdaScale and YOLOv3. We find that ApproxDet is able to adapt to a wide variety of contention and content characteristics and outshines all baselines, e.g., it achieves 52% lower latency and 11.1% higher accuracy over YOLOv3. Our software is open-sourced at https://github.com/purdue-dcsl/ApproxDet.

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Cited By

Zhou SHernandez AGomez CYin WBjörkman M(2025)SmartTBD: Smart Tracking for Resource-constrained Object DetectionACM Transactions on Embedded Computing Systems10.1145/370391224:2(1-19)Online publication date: 31-Mar-2025
https://dl.acm.org/doi/10.1145/3703912
Wang PBagchi SLi YChaterji S(2025)Adapt3D: Lightweight and Adaptive 3D Detection Framework for Mobile GPUs2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885676(823-827)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885676
Machidon APejović V(2025)Integrating control-theoretic predictive deep learning for resource-efficient computing systemsJournal of Intelligent Information Systems10.1007/s10844-025-00919-7Online publication date: 27-Jan-2025
https://doi.org/10.1007/s10844-025-00919-7
Show More Cited By

Index Terms

ApproxDet: content and contention-aware approximate object detection for mobiles
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
        Tracking
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

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DOI:10.1145/3384419

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https://dl.acm.org/doi/10.1145/3703912
Wang PBagchi SLi YChaterji S(2025)Adapt3D: Lightweight and Adaptive 3D Detection Framework for Mobile GPUs2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885676(823-827)Online publication date: 6-Jan-2025
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https://doi.org/10.1007/s10844-025-00919-7
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