Abstract
The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper.
Highlights
The ultimate goal of our study is to increase driver’s safety by alerting the driver when driving under non-ideal conditions using a vision-based Advanced Drivers Assistance (ADAS)
While not every vehicle is equipped with an expensive radar detection system, most cars are equipped with video driving recorders which can readily supply video input for processing
The LBP-based Adaboost Classifier was tested, followed by the second experiment to test the rectified Multi-Scale Retinex (MSR); the third, which is to test the BoF classifiers; followed by the fourth, which is to compare the Convolutional Neural Network (CNN)-based classifier against similar features; the fifth, which is a comparison experiment between the BoF and the CNN-based classifiers in when the input images are crisp and cleanly extracted
Summary
The ultimate goal of our study is to increase driver’s safety by alerting the driver when driving under non-ideal conditions using a vision-based ADAS. Advanced Drivers Assistance (ADAS) is slowly reaching technological maturity. There are already many ADAS models that use the video driving recorder as a sensor, for example, for detecting pedestrians [1]. Collision Warning (FCW) radar [2], as well as other collision avoidance systems [3] such as the intelligent reversing radar systems to warn the driver while moving. While not every vehicle is equipped with an expensive radar detection system, most cars are equipped with video driving recorders which can readily supply video input for processing.
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