Mr. Chia Hong Khoo Profile

Chia Hong Khoo

at Univ Tunku Abdul Rahman

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Proceedings Article | 1 May 2022 Paper

Action detection system for alerting drivers using computer vision

Chia Hong Khoo, Phooi Yee Lau

Proceedings Volume 12177, 121770K (2022) https://doi.org/10.1117/12.2625232

KEYWORDS: Convolution, Roads, Machine vision, Computing systems, Computer vision technology, Classification systems, Video, Prototyping, Image processing, Imaging systems

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Nowadays, the increasing number of careless drivers on the road has resulted in more accident cases. Driver’s decisions and behaviors are the key to maintain road safety. However, many drivers tend to do secondary tasks like playing with their phone, adjusting radio player, eating or drinking, answering phone calls, and worse case is reading phone texts. In previous efforts, many kinds of approaches had been introduced to try to solve the task to recognize and capture potential problem related to careless driving inside the car. In this project, the work will mainly focus on the driver's secondary task recognition using the action detection method. A camera will be set up inside the car for the real-time extract of driver’s action. The video will undergo a process to extract out the human pose frames without background, called human pose estimator framework. Inside this framework, raw images will be input into a convolutional neural network (CNN) that computes human key-point's activation maps. After, that key-point's coordinate will be computed using the output activation maps and drawn on a new blank frame. Then the frames will be input into classification CNN network for the action classification. If an action performed by driver is considered a dangerous secondary task, alert will be given. The proposed framework was able to achieve a higher speed compared to other people's framework if it is being run on Raspberry Pi CPU. It is able to detect 10 different driver actions where only talking to passengers and normal driving will not trigger the buzzer to give alert to driver.

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