Introduction

In recent years, the number of high buildings in the city is increasing, and accidents at the edge of high buildings occur frequently. Timely and effective detection and recognization of high-rise dangerous actions can protect people's lives. However, the detection and recognization of high-rise actions is rarely studied because of the following reasons: (1) The background is complex and changeable (e.g. illumination and weather variation); (2) The human body targets at the edge of high-rise buildings in the surveillance video are small, and the human body is partially occluded; (3) There is no benchmark dataset (the most critical factor) specialized for spatio-temporal detection for high-rise human action. To address these issues, we construct a benchmark video dataset termed as STD-HA, the first one for spatio-temporal detection for high-rise human action. Our dataset is diverse, which contains 9 action categories from multiple scenes with four weather conditions.

Examples

Download Dataset

Download link for dataset: [ Google Drive]