@conference{telfor1,
title = {Driver monitoring algorithm for Advanced Driver Assistance Systems},
author = {Aleksandra Simić and Ognjen Kocić and Milan Z. Bjelica and Milena Milošević},
doi = {10.1109/TELFOR.2016.7818908},
isbn = {978-1-5090-4086-5},
year = {2016},
date = {2016-11-22},
booktitle = {Telecommunications Forum (TELFOR), 2016 24th},
publisher = {IEEE},
address = {Belgrade, Serbia},
abstract = {Fast expansion of Advanced Driver Assistance Systems (ADAS) market and applications has resulted in a high demand for various accompanying algorithms. In this paper we present an implementation of Driver monitoring algorithm. Main goal of the algorithm is to automatically asses if driver is tired and in that case, raise a proper alert. It is widely used as a standard component of rest recommendation systems. Our approach is based on combination of computer vision algorithms for face detection and eyes detection. Additionally, we have tested our implementation in controlled environment on a real ADAS platform board.},
howpublished = {M33},
keywords = {ADAS, automotive, computer vision, driver monitoring, ieeexplore, image processing},
pubstate = {published},
tppubtype = {conference}
}
Fast expansion of Advanced Driver Assistance Systems (ADAS) market and applications has resulted in a high demand for various accompanying algorithms. In this paper we present an implementation of Driver monitoring algorithm. Main goal of the algorithm is to automatically asses if driver is tired and in that case, raise a proper alert. It is widely used as a standard component of rest recommendation systems. Our approach is based on combination of computer vision algorithms for face detection and eyes detection. Additionally, we have tested our implementation in controlled environment on a real ADAS platform board.
@conference{telfor2,
title = {Optimization of driver monitoring ADAS algorithm for heterogeneous platform},
author = {Ognjen Kocić and Aleksandra Simić and Milan Z. Bjelica and Tomislav Maruna},
doi = {10.1109/TELFOR.2016.7818910},
isbn = {978-1-5090-4086-5},
year = {2016},
date = {2016-11-22},
booktitle = {Telecommunications Forum (TELFOR), 2016 24th},
publisher = {IEEE},
address = {Belgrade, Serbia},
abstract = {Rapid expansion of Advanced Driver Assistance Systems (ADAS) applications has resulted in development of many new algorithms that are applied in solving various challenging problems. These algorithms need to be implemented on existing ADAS platforms which are usually heterogeneous in order to maximize computing power, while minimizing power consumption. The problem becomes how to efficiently decouple the algorithm and map parts of it to heterogeneous hardware, often including CPU, DSP and GPU blocks. This paper gives some insight into efficient ADAS algorithms mappings and optimizations for these platforms. As an illustrative example, driver monitoring algorithm is optimized.},
howpublished = {M33},
keywords = {ADAS, automotive, driver monitoring, ieeexplore},
pubstate = {published},
tppubtype = {conference}
}
Rapid expansion of Advanced Driver Assistance Systems (ADAS) applications has resulted in development of many new algorithms that are applied in solving various challenging problems. These algorithms need to be implemented on existing ADAS platforms which are usually heterogeneous in order to maximize computing power, while minimizing power consumption. The problem becomes how to efficiently decouple the algorithm and map parts of it to heterogeneous hardware, often including CPU, DSP and GPU blocks. This paper gives some insight into efficient ADAS algorithms mappings and optimizations for these platforms. As an illustrative example, driver monitoring algorithm is optimized.