FADE: A Dataset for Detecting Falling Objects around Buildings in Video
This addresses a safety problem for pedestrians by improving surveillance detection of falling objects, but it is incremental as it focuses on dataset creation and method adaptation.
They tackled the lack of a large-scale dataset for detecting falling objects around buildings in video, proposing the FADE dataset with 2,611 videos from 25 scenes and 8 object categories, and developed a novel detection method that outperformed state-of-the-art approaches.
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/.