用360度摄像头检测视障者周围异常事件，提升安全防护能力。
Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Camera
- 基于360度摄像头捕捉全景画面，实时识别异常行为。
- 在VIEW360等数据集上达到当前最优检测性能。
- 适合开发视障辅助设备或智能安全系统的研究者。
近年来，计算机视觉技术的发展重新激发了为视障人士开发辅助技术的兴趣。尽管已有大量研究聚焦于图像场景理解，但对视障者实际安全与隐私问题的关注仍不足。为此，我们首次提出利用第一人称360度摄像头全面感知视障者周围环境，以检测异常情况。我们构建了首个相关视频数据集VIEW360（Visually Impaired Equipped with Wearable 360-degree camera），包含肩窥、扒窃等视障者可能遭遇的异常行为。同时提出新型网络架构FDPN（Frame and Direction Prediction Network），实现帧级异常事件预测及方向定位。在自建的VIEW360数据集以及公开的UCF-Crime和Shanghaitech数据集上评估，结果表明该方法性能达到当前领先水平。
原文摘要 · Abstract (English)
Recent advancements in computer vision have led to a renewed interest in developing assistive technologies for individuals with visual impairments. Although extensive research has been conducted in the field of computer vision-based assistive technologies, most of the focus has been on understanding contexts in images, rather than addressing their physical safety and security concerns. To address this challenge, we propose the first step towards detecting anomalous situations for visually impaired people by observing their entire surroundings using an egocentric 360-degree camera. We first introduce a novel egocentric 360-degree video dataset called VIEW360 (Visually Impaired Equipped with Wearable 360-degree camera), which contains abnormal activities that visually impaired individuals may encounter, such as shoulder surfing and pickpocketing. Furthermore, we propose a new architecture called the FDPN (Frame and Direction Prediction Network), which facilitates frame-level prediction of abnormal events and identifying of their directions. Finally, we evaluate our approach on our VIEW360 dataset and the publicly available UCF-Crime and Shanghaitech datasets, demonstrating state-of-the-art performance.
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