# ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles

arXiv: https://arxiv.org/abs/2010.10754 · HTML: https://mlsanity.com/paper/2010.10754/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception](https://mlsanity.com/paper/2106.05665/) (2021)
- [Event Neural Networks](https://mlsanity.com/paper/2112.00891/) (2021)
- [AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics](https://mlsanity.com/paper/2301.08664/) (2023)
- [DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers](https://mlsanity.com/paper/2107.10404/) (2021)
- [DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment](https://mlsanity.com/paper/2508.06041/) (2025)
- [Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge Devices](https://mlsanity.com/paper/2410.01270/) (2024)
- [MMEdge: Accelerating On-device Multimodal Inference via Pipelined Sensing and Encoding](https://mlsanity.com/paper/2510.25327/) (2025)
- [ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy](https://mlsanity.com/paper/2210.16083/) (2022)
- [SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection](https://mlsanity.com/paper/2201.04235/) (2022)
- [Virtuoso: Video-based Intelligence for real-time tuning on SOCs](https://mlsanity.com/paper/2112.13076/) (2021)
- [SAWEC: Sensing-Assisted Wireless Edge Computing](https://mlsanity.com/paper/2402.10021/) (2024)
- [Mondrian: On-Device High-Performance Video Analytics with Compressive Packed Inference](https://mlsanity.com/paper/2403.07598/) (2024)
- [HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices](https://mlsanity.com/paper/2411.00608/) (2024)
- [A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications](https://mlsanity.com/paper/2507.15628/) (2025)

## Abstract (arXiv)

Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous transportation. With an ever-growing number of powerful client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices to achieve 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 (layered on Faster R-CNN), 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 couple this kernel with approximable video object tracking algorithms to create an end-to-end video object detection system. 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.