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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition
open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, driversā activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driverās distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context
Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification
Accurate, robust, inexpensive gaze tracking in the car can help keep a driver
safe by facilitating the more effective study of how to improve (1) vehicle
interfaces and (2) the design of future Advanced Driver Assistance Systems. In
this paper, we estimate head pose and eye pose from monocular video using
methods developed extensively in prior work and ask two new interesting
questions. First, how much better can we classify driver gaze using head and
eye pose versus just using head pose? Second, are there individual-specific
gaze strategies that strongly correlate with how much gaze classification
improves with the addition of eye pose information? We answer these questions
by evaluating data drawn from an on-road study of 40 drivers. The main insight
of the paper is conveyed through the analogy of an "owl" and "lizard" which
describes the degree to which the eyes and the head move when shifting gaze.
When the head moves a lot ("owl"), not much classification improvement is
attained by estimating eye pose on top of head pose. On the other hand, when
the head stays still and only the eyes move ("lizard"), classification accuracy
increases significantly from adding in eye pose. We characterize how that
accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note:
text overlap with arXiv:1507.0476
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