Dr. Zalhan Mohd Zin Profile

Dr. Zalhan Mohd Zin

at Univ Kuala Lumpur Malaysia France Institute

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Proceedings Article | 15 March 2019 Paper

Vision based eye closeness classification for driver’s distraction and drowsiness using PERCLOS and support vector machines

Zalhan Mohd Zin, Anis Hazirah Rodzi, Norazlin Ibrahim

Proceedings Volume 11041, 110410G (2019) https://doi.org/10.1117/12.2522949

KEYWORDS: Eyeglasses, Eye, Image classification, Roads, Machine vision, Computer vision technology, Feature extraction, Safety

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Driver inattention and drowsiness are part causes of road accidents in Malaysia. Statistics shows that about 3 deaths per 10 000 registered vehicles reported by Malaysian Institute of Road Safety Research (MIROS) in 2016. Hence, an assistant system is needed to monitor driver’s condition like some car manufacturers introduced to their certain models of car. The assistant system is a part of main system known as advanced driver assistance systems (ADAS) are systems developed to enhance vehicle systems for safety and better driving. Desire to build safer vehicles and roads to reduce the number of fatalities and by legislation cause demand for ADAS. However, there are several challenges to design, implement, deploy, and operate ADAS. The system is expected to gather accurate input, be fast in processing data, accurately predict context, and react in real time. Suitable approach is needed to fulfil the system expectation. There are four types of detection including by using physiological sensors, driver performance, computer vision, and hybrid system. This paper describes the drowsiness and driver in attention detection and classification using computer vision approach. Our approach aims to classify driver drowsiness and inattention using computer vision. We proposed a technique to classify drowsiness into three different classes of eye state; open, semi close and close. The classification is done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. Two types of data training and testing images of drivers’ eye condition which are eye with spectacles and eye without spectacles have been used. The results show that the proposed technique can classify the classes of distraction or drowsiness with high accuracy. Furthermore, by using only one type of eye condition data training, we are also able to classify the three different drowsiness classes regardless the eye conditions.

Proceedings Article | 9 July 2011 Paper

Vision-based obstacle recognition system for automated lawn mower robot development

Zalhan Mohd Zin, Ratnawati Ibrahim

Proceedings Volume 8009, 80092K (2011) https://doi.org/10.1117/12.896146

KEYWORDS: Image processing, Image segmentation, Neodymium, Image filtering, Robot vision, Databases, Digital image processing, Intelligence systems, Navigation systems, Sensors

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Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

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