# OmniSense: Towards Edge-Assisted Online Analytics for 360-Degree Videos

arXiv: https://arxiv.org/abs/2508.14237 · HTML: https://mlsanity.com/paper/2508.14237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Tangent Images for Mitigating Spherical Distortion](https://mlsanity.com/paper/1912.09390/) (2019)
- [Cube Padding for Weakly-Supervised Saliency Prediction in 360deg Videos](https://mlsanity.com/paper/1806.01320/) (2018)
- [SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images](https://mlsanity.com/paper/1811.08196/) (2018)
- [Scaling Video Analytics on Constrained Edge Nodes](https://mlsanity.com/paper/1905.13536/) (2019)
- [360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images](https://mlsanity.com/paper/1910.01712/) (2019)
- [Deep 360 Pilot: Learning a Deep Agent for Piloting through 360deg Sports Video](https://mlsanity.com/paper/1705.01759/) (2017)
- [Object Detection in Equirectangular Panorama](https://mlsanity.com/paper/1805.08009/) (2018)
- [CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale](https://mlsanity.com/paper/2105.06524/) (2021)
- [Pano: Optimizing 360deg Video Streaming with a Better Understanding of Quality Perception](https://mlsanity.com/paper/1911.04139/) (2019)

## Abstract (arXiv)

With the reduced hardware costs of omnidirectional cameras and the proliferation of various extended reality applications, more and more 360^circ videos are being captured. To fully unleash their potential, advanced video analytics is expected to extract actionable insights and situational knowledge without blind spots from the videos. In this paper, we present OmniSense, a novel edge-assisted framework for online immersive video analytics. OmniSense achieves both low latency and high accuracy, combating the significant computation and network resource challenges of analyzing 360^circ videos. Motivated by our measurement insights into 360^circ videos, OmniSense introduces a lightweight spherical region of interest (SRoI) prediction algorithm to prune redundant information in 360^circ frames. Incorporating the video content and network dynamics, it then smartly scales vision models to analyze the predicted SRoIs with optimized resource utilization. We implement a prototype of OmniSense with commodity devices and evaluate it on diverse real-world collected 360^circ videos. Extensive evaluation results show that compared to resource-agnostic baselines, it improves the accuracy by 19.8% -- 114.6% with similar end-to-end latencies. Meanwhile, it hits 2.0× -- 2.4× speedups while keeping the accuracy on par with the highest accuracy of baselines.