Objects falling from buildings, a frequently occurring event in daily life, can cause severe injuries to pedestrians due to the high impact force they exert. Surveillance cameras are often installed around buildings to detect falling objects, but such detection remains challenging due to the small size and fast motion of the objects. Moreover, the field of falling object detection around buildings (FODB) lacks a large-scale dataset for training learning-based detection methods and for standardized evaluation. To address these challenges, we propose a large and diverse video benchmark dataset named FADE. Specifically, FADE contains 2,611 videos from 25 scenes, featuring 8 falling object categories, 4 weather conditions, and 4 video resolutions. Additionally, we develop a novel detection method for FODB that effectively leverages motion information and generates small-sized yet high-quality detection proposals. The efficacy of our method is evaluated on the proposed FADE dataset by comparing it with state-of-the-art approaches in generic object detection, video object detection, and moving object detection. The dataset and code are publicly available at https://fadedataset.github.io/FADE.github.io/.
@article{arxiv.2408.05750,
title = {FADE: A Dataset for Detecting Falling Objects around Buildings in Video},
author = {Zhigang Tu and Zhengbo Zhang and Zitao Gao and Chunluan Zhou and Junsong Yuan and Bo Du},
journal= {arXiv preprint arXiv:2408.05750},
year = {2025}
}
Comments
Accepted by IEEE Transactions on Information Forensics and Security (TIFS), 2025