Falling objects (even small ones) from the high-rise buildings, which have the characteristics of the fast moving speed and short collision time, would cause severe harm to the people who pass by, as the falling objects can produce tremendous impact force. However, due to the reasons that the background is complicated and changeable (e.g. illumination and weather variation), falling objects in the monitoring video are small and sometimes blurred due to fast motion, and there is no available benchmark dataset specialized for falling object detection (the most crucial factor), falling object detection is rarely studied and the accuracy is low. To address these issues, we construct a benchmark video dataset termed as FADE, the first one for FAlling object DEtection around buildings. Our dataset is diverse, which contains ten object categories from 17 scenes with four weather conditions, and four video resolutions.
• videos in diverse weather conditions, light conditions, and scenes.
• 10 kinds of objects as follows: clothes, basins, shoes, kitchen waste, water balloons, books, spitballs, bottles, packaging bags, and packaging boxes.
• three different camera angles: 30°, 45°, and 60°.
• videos with four resolutions: 1280 × 720, 1920 × 1080, 2560 × 1440, and 2592 × 1520.
• data with diverse backgrounds.
License: Our FADE dataset is published under the CC BY-NC-SA 4.0 license. Our code is released under the Apache 2.0 license.