Open source platform enables low cost 360 video eye tracking crowdsourcing
OpenSAL360: Open-Source Crowdsourcing Platform for Omnidirectional Video Saliency Collection
Computer Vision and Pattern Recognition
Summary
Collecting where people look in virtual reality videos usually needs expensive VR headsets, making large studies hard to do. The authors designed OpenSAL360, a free tool that uses ordinary computer screens and a mouse to gather this information from many online participants cheaply. They checked that this new approach matches data from traditional VR eye tracking and used it to collect the largest 360° video eye-tracking dataset so far. This makes it easier to study and improve immersive video experiences without costly equipment.
What this means in practice
- •For immersive media developers: Create large-scale eye gaze datasets for optimizing 360° video compression and streaming without costly VR hardware.
- •For user interface designers: Collect saliency data cheaply to improve visual attention models for virtual environments by leveraging general online crowdsourcing.
Authors
Alexey Bryncev, Andrey Moskalenko, Kira Shilovskaya, Ivan Kosmynin, Dmitriy Vatolin
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.