用游戏生成致命暴力视频数据，提升真实监控中凶案检测效果
GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation
- 用GTA5游戏生成多视角、多环境的致命暴力视频数据
- 结合对抗性片段级域自适应，使合成数据有效提升真实检测精度
- 数据集与生成框架开源，适合安防与视频分析研究者使用
近期视频异常检测进展使得各类犯罪行为识别成为可能，但因枪击、刺杀等致命事件罕见且采集存在伦理问题，检测仍具挑战。为此，我们提出GTA-Crime，一个基于《侠盗猎车手5》（GTA5）的致命暴力视频数据集与生成框架。该数据集包含枪击、刺杀等场景，从多种监控视角、不同动作类型、天气、时段和视角下采集。为应对此类事件稀少的问题，我们还发布了视频生成框架。此外，提出基于Wasserstein对抗训练的片段级域自适应策略，缩小合成数据与真实数据（如UCF-Crime）特征差异。实验验证了GTA-Crime数据集的有效性，表明结合该框架可持续提升真实世界致命暴力检测准确率。数据集与生成框架已公开于https://github.com/ta-ho/GTA-Crime。
原文摘要 · Abstract (English)
Recent advancements in video anomaly detection (VAD) have enabled identification of various criminal activities in surveillance videos, but detecting fatal incidents such as shootings and stabbings remains difficult due to their rarity and ethical issues in data collection. Recognizing this limitation, we introduce GTA-Crime, a fatal video anomaly dataset and generation framework using Grand Theft Auto 5 (GTA5). Our dataset contains fatal situations such as shootings and stabbings, captured from CCTV multiview perspectives under diverse conditions including action types, weather, time of day, and viewpoints. To address the rarity of such scenarios, we also release a framework for generating these types of videos. Additionally, we propose a snippet-level domain adaptation strategy using Wasserstein adversarial training to bridge the gap between synthetic GTA-Crime features and real-world features like UCF-Crime. Experimental results validate our GTA-Crime dataset and demonstrate that incorporating GTA-Crime with our domain adaptation strategy consistently enhances real world fatal violence detection accuracy. Our dataset and the data generation framework are publicly available at https://github.com/ta-ho/GTA-Crime.
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