[2C6-GS-7] Language media processing:

[2C6-GS-7-01] Accident risk estimation with depth information in construction site videos

〇Ryota Goka¹, Keisuke Maeda¹, Ren Togo¹, Takahiro Ogawa¹, Miki Haseyama¹ (1. Hokkaido University)

Keywords:deep learning, video processing, accident risk estimation

In the construction industry, reducing accident risk and improving safety is one of the high-priority tasks. Recently, several methods have been proposed to estimate the contact accident risk with heavy machinery on construction sites for enhancing safety. Conventional studies based on deep learning estimate the risk by using relations within the image space of detected workers and machinery captured in construction site videos. However, these approaches focus on the distance between detected objects in the image, leading to the problem that accident risk is overestimated even when there is distance between objects in the real world. In this study, to consider 3D spatial information in videos, we propose a method for estimating the accident risk with visual features regarding depth information. Experimental results show that the proposed method performs better than existing methods in estimating the contact accident risk.