999 Part 1(1).mp4 -

The video is part of a study that addresses the high rate of accidents in the construction industry. Unlike traditional sensors that fire an alarm whenever any object is near, DCAS uses a to evaluate risk dynamically based on:

: The study noted that moving machine parts (like an excavator's arm) can sometimes obstruct the view or cause perspective distortion, leading to slight distance errors.

: By using the known size of objects and camera focal lengths, the system can estimate the distance of a worker or machine within a small margin of error. 999 Part 1(1).mp4

: The system significantly decreased the number of "nuisance" alarms compared to static sensors, as it understands when a worker or another machine is approaching safely for collaboration.

: Recognizes if a worker is facing away or kneeling, which increases risk. The video is part of a study that

: Distinguishes between workers, excavators, and forklifts.

: To save time, researchers used the virtual environment to automatically generate bounding boxes around objects, ensuring high precision for the AI training. Key Findings from the Research : The system significantly decreased the number of

: Adjusts risk based on where the camera is mounted on the machine (e.g., blind spots). How the Video Was Created