SPICA: Interactive Video Content Exploration through Augmented Audio Descriptions for Blind or Low-Vision Viewers
<|reference_start|>SPICA: Interactive Video Content Exploration through Augmented Audio Descriptions for Blind or Low-Vision Viewers: Blind or Low-Vision (BLV) users often rely on audio descriptions (AD) to access video content. However, conventional static ADs can leave out detailed information in videos, impose a high mental load, neglect the diverse needs and preferences of BLV users, and lack immersion. To tackle these challenges, we introduce SPICA, an AI-powered system that enables BLV users to interactively explore video content. Informed by prior empirical studies on BLV video consumption, SPICA offers novel interactive mechanisms for supporting temporal navigation of frame captions and spatial exploration of objects within key frames. Leveraging an audio-visual machine learning pipeline, SPICA augments existing ADs by adding interactivity, spatial sound effects, and individual object descriptions without requiring additional human annotation. Through a user study with 14 BLV participants, we evaluated the usability and usefulness of SPICA and explored user behaviors, preferences, and mental models when interacting with augmented ADs.<|reference_end|>
@article{ning2024spica:,
    title={SPICA: Interactive Video Content Exploration through Augmented Audio
  Descriptions for Blind or Low-Vision Viewers},
    author={Zheng Ning, Brianna L. Wimer, Kaiwen Jiang, Keyi Chen, Jerrick Ban,
  Yapeng Tian, Yuhang Zhao, Toby Jia-Jun Li},
    journal={arXiv preprint arXiv:2402.07300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07300},
    primaryClass={cs.HC cs.MM}
}