构建儿童危险行为识别数据集，提升安全监控能力
KidRisk: Benchmark Dataset for Children Dangerous Action Recognition
- 构建包含2500段视频、10000张图像的儿童危险行为数据集
- 视觉语言模型在危险动作识别上准确率达96.14%，显著优于传统方法
- 适合儿童安全监护、智能视频分析领域研究者参考
儿童天性活泼，在自主活动中常面临潜在危险，尤其在缺乏家长监督时。准确识别高风险动作对保障其安全至关重要。本文构建了一个新挑战性数据集KidRisk，包含2500段儿童行为短视频和10000张儿童危险动作图像。我们在该数据集上建立了基准测试，发现传统深度学习模型在此任务上表现有限。为此，我们提出了基于视觉-语言的基线模型，具备更强的上下文理解能力。所提方法在儿童行为分类上达到83.53%准确率，在危险动作识别上达96.14%，显著优于传统方法。结果表明，视觉-语言模型不仅可行，且在检测危险行为方面极具有效性，有助于提升儿童安全保障水平。
原文摘要 · Abstract (English)
Children are naturally energetic, and during their spontaneous activities, they often encounter potentially dangerous situations, especially when lacking parental supervision. Identifying actions that pose risks plays a crucial role in ensuring their safety. This paper build a novel challenging dataset, namely KidRisk, including 2,500 short videos of children's actions and 10,000 images for dangerous action of children. We also introduce a benchmark on our newly constructs dataset and find that traditional deep learning models demonstrated limited effectiveness on these tasks. Therefore, we develop vision-language based baselines with exceptional context understanding of visual information. Our proposed methods achieved an accuracy of 83.53% in classifying children's actions and 96.14% in recognizing children's dangerous actions, significantly outperforming traditional approaches. These results confirm that vision-language models are not only feasible but also highly effective in detecting hazardous actions, contributing positively to safeguarding children's safety.
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