Advanced Driver Assistance systems (ADAS) have seen increasing popularity due to their importance in automotive driver safety and autonomous driving. These systems analyze data from several sensors mounted on the vehicle to detect lanes, obstacles and traffic conditions to ensure safe driving. Lane markings on road with different color and structure provide information on safe drive zone and other traffic restrictions on road. Typical ADAS solutions depend on vision based sensors for lane detection.Here we propose an efficient algorithm for detecting type and color of the lane marks as this information plays critical role in taking the decision for safety features such as lane change and lane keep assist. Our algorithm is pluggable to any state-of-art lane detection algorithm and provides lane type and color for straight, curvy roads. The proposed method is tested on various challenging scenarios and results are promising.
Eye gaze estimation aims to find the point of gaze which is basically," where we look". Estimating the gaze point plays an important role in many applications with varying usage. Gaze estimation is used in automotive industry to ensure safety. In the field of retail shopping and online marketing gaze estimation is used to analyse the consumer's interest and focus. Gaze estimation is also used for psychological tests and in healthcare for diagnosing some of the neurological disorders. This also has a significant role to play in the field to entertainment. There are multiple ways by which eye gaze estimation can be done. This paper is about a comparative study done on two of the popular methods for gaze estimation using eye features. An infra-red camera is used to capture data for this study. Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze. There are advantages and disadvantages with both the methods which has been looked into. Choosing the right method for gaze estimation hence depends on the type of application, precision required and many other factors including environmental conditions. This paper can act as a reference for researchers working in the same field
Driver monitoring system has gained lot of popularity in automotive sector to ensure safety while driving. Collisions due to driver inattentiveness or driver fatigue or over reliance on autonomous driving features arethe major reasons for road accidents and fatalities. Driver monitoring systems aims to monitor various aspect of driving and provides appropriate warnings whenever required. Eye gaze estimation is a key element in almost all of the driver monitoring systems. Gaze estimation aims to find the point of gaze which is basically,” -where is driver looking”. This helps in understanding if the driver is attentively looking at the road or if he is distracted. Estimating gaze point also plays important role in many other applications like retail shopping, online marketing, psychological tests, healthcare etc. This paper covers the various aspects of eye gaze estimation for a driver monitoring system including sensor choice and sensor placement. There are multiple ways by which eye gaze estimation can be done. A detailed comparative study on two of the popular methods for gaze estimation using eye features is covered in this paper. An infra-red camera is used to capture data for this study. Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze. There are advantages and disadvantages with both the methods which has been looked into. This paper can act as a reference for researchers working in the same field to understand possibilities and limitations of eye gaze estimation for driver monitoring system.
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