DocumentCode :
181656
Title :
Night vision animal detection
Author :
Forslund, David ; Bjarkefur, Jon
Author_Institution :
Autoliv Electron. Sweden, Linköping, Sweden
fYear :
2014
fDate :
8-11 June 2014
Firstpage :
737
Lastpage :
742
Abstract :
In order to reduce traffic accidents involving animals, which is a major concern in worldwide traffic, Autoliv has developed a state-of-the-art vehicle mounted night vision animal detection system. The system is currently used by Audi, BMW and Daimler. The main contributions of this paper include: world´s first vehicular animal detection system to reach the customer market, an efficient classification approach based on a cascade boosting concept which is robust to occlusion, pose and scale variations, a large database of thousands of hours of far infrared (FIR) video data recorded worldwide including several hundred thousand example images of animals in traffic situations, a tracking approach to handle animal movement and estimate animal states, a validation approach to efficiently reduce the number of false detections and human-machine-interface (HMI) and warning concepts to highlight animals at risk of collision. The presented system detects animals up to 200 meters away from the car while generating very few false warnings. For animals that are considered a potential danger, advanced HMIs such as marking lights which actively illuminates the animals are applied, giving the driver the quick and accurate information he or she requires. The Autoliv night vision animal detection system is complementary to currently used methods for preventing accidents with animals. By using it, the driver is given all opportunities to react to dangerous situations and to avoid potential accidents.
Keywords :
accident prevention; automobiles; driver information systems; image classification; image motion analysis; night vision; object detection; object tracking; risk management; road accidents; road safety; road traffic; state estimation; Audi; Autoliv night vision animal detection system; BMW; Daimler; FIR video data; HMI; accident prevention; animal illumination; animal images; animal movement handling; animal state estimation; car; cascade boosting concept; classification approach; collision risk; customer market; dangerous situations; driver information; false detections; false warnings; far infrared video data; human-machine-interface; marking lights; occlusion; pose variation; potential accident avoidance; potential danger; scale variation; tracking approach; traffic accident reduction; traffic situations; validation approach; vehicle mounted night vision animal detection system; vehicular animal detection system; warning concept; Accidents; Animals; Cameras; Finite impulse response filters; Roads; Vehicle crash testing; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium Proceedings, 2014 IEEE
Conference_Location :
Dearborn, MI
Type :
conf
DOI :
10.1109/IVS.2014.6856446
Filename :
6856446
Link To Document :
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