FADE: A Dataset for Detecting Falling Objects around Buildings in Video

Falling objects from buildings can cause severe injuries to pedestrians due to the great impact force they exert. Although surveillance cameras are installed around some buildings, it is challenging for humans to capture such events in surveillance videos due to the small size and fast motion of falling objects, as well as the complex background. Therefore, it is necessary to develop methods to automatically detect falling objects around buildings in surveillance videos. To facilitate the investigation of falling object detection, we propose a large, diverse video dataset called FADE (FAlling Object DEtection around Buildings) for the first time. FADE contains 1,881 videos from 18 scenes, featuring 8 falling object categories, 4 weather conditions, and 4 video resolutions. Additionally, we develop a new object detection method called FADE-Net, which effectively leverages motion information and produces small-sized but high-quality proposals for detecting falling objects around buildings. Importantly, our method is extensively evaluated and analyzed by comparing it with the previous approaches used for generic object detection, video object detection, and moving object detection on the FADE dataset. Experimental results show that the proposed FADE-Net significantly outperforms other methods, providing an effective baseline for future research. The dataset and code are publicly available at https://fadedataset.github.io/FADE.github.io/.

高空坠物因其产生的巨大冲击力，会对行人造成严重伤害。尽管部分建筑周边已部署监控摄像头，但由于坠物体积小巧、运动速度快且背景复杂，人工从监控视频中捕捉此类事件极具挑战性。因此，研发可自动检测建筑周边监控视频中坠物的方法具有重要现实意义。为推动坠物检测领域的研究进展，我们首次提出了一个大规模、多样化的视频数据集——FADE（FAlling Object DEtection around Buildings，建筑周边坠物检测数据集）。该数据集包含来自18个场景的1881段视频，涵盖8类坠物、4种天气条件以及4种视频分辨率。此外，我们还提出了一种全新的目标检测方法FADE-Net，该方法能够有效利用运动信息，生成高质量的小尺寸候选框，用于检测建筑周边的坠物。值得注意的是，我们通过将FADE-Net与当前通用目标检测、视频目标检测以及运动目标检测领域的主流方法在FADE数据集上进行对比，开展了全面的评估与分析。实验结果表明，所提出的FADE-Net性能显著优于其他同类方法，为后续相关研究提供了有效的基准基线。本数据集与代码已在https://fadedataset.github.io/FADE.github.io/ 公开获取。

搜集汇总

背景与挑战

背景概述

FADE数据集是一个用于检测建筑物周围视频中坠落物体的大规模多样化视频数据集，包含1,881个视频，覆盖18个场景、8种物体类别、4种天气条件和4种视频分辨率。该数据集还提供了一个名为FADE-Net的新检测方法，利用运动信息生成高质量提案，显著优于其他方法，为未来研究提供了有效基线。