OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection

2026-09-18

AI Summary

为解决全景视频显著性数据收集成本高、复杂度大的问题，提出OpenSAL360平台，利用普通屏幕和鼠标通过众包方式低成本收集数据。

Abstract

Omnidirectional video saliency prediction plays an important role in many immersive multimedia applications, including viewport-adaptive streaming and compression, foveated rendering, mesh simplification, perceptual quality assessment. Yet progress in this area remains constrained by the cost and complexity of collecting eye-tracking data with VR headsets, which makes large-scale dataset creation difficult to extend. We present OpenSAL360, the first open-source platform for scalable, low-cost 360° video saliency collection. Unlike conventional VR-based protocols, it requires only a standard screen, mouse, and internet connection, enabling parallel saliency data collection from common crowdsourcing assessors without specialized hardware. We validate our collection protocol against seven well-established VR eye-tracking datasets and conduct ablation studies on key interface, pre-, and post-processing parameters. To demonstrate the effectiveness and scalability of the proposed methodology, we collect and publicly release a saliency dataset covering 500 omnidirectional videos annotated by 2,000+ crowdsourcing assessors, making it, to the best of our knowledge, the largest dataset in this field. We make OpenSAL360 publicly available at https://github.com/msu-video-group/OpenSAL360.

Problem

• omnidirectional video
• saliency prediction
• eye-tracking data
• VR headsets
• large-scale dataset

Innovation

• omnidirectional video saliency
• open-source platform
• scalable and low-cost data collection
• crowdsourcing