Adaptive Randomized Hough Transform for Circle Detection using Moving Window

Guo, Siyu; Zhang, Xu-Fang; Zhang, Fan

doi:10.1109/icmlc.2006.258739

Cited by 25 publications

(12 citation statements)

References 11 publications

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“…PERCLOS was specifically defined by three criteria, P70, P80, and EM. It turns out to be maximum in correlation coefficient between P80 in ve results from fatigue [10]. The definitions of the three PERCLOS measures are as follows:…”

Section: Brief Introduction Of Perclosmentioning

confidence: 99%

Driver fatigue detection based on eye state

Lin¹,

Huang²,

Ni³

et al. 2015

THC

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Abstract. BACKGROUND: Nowadays, more and more traffic accidents occur because of driver fatigue. OBJECTIVE: In order to reduce and prevent it, in this study, a calculation method using PERCLOS (percentage of eye closure time) parameter characteristics based on machine vision was developed. It determined whether a driver's eyes were in a fatigue state according to the PERCLOS value. METHODS: The overall workflow solutions included face detection and tracking, detection and location of the human eye, human eye tracking, eye state recognition, and driver fatigue testing. The key aspects of the detection system incorporated the detection and location of human eyes and driver fatigue testing. The simplified method of measuring the PERCLOS value of the driver was to calculate the ratio of the eyes being open and closed with the total number of frames for a given period. RESULTS: If the eyes were closed more than the set threshold in the total number of frames, the system would alert the driver. CONCLUSION: Through many experiments, it was shown that besides the simple detection algorithm, the rapid computing speed, and the high detection and recognition accuracies of the system, the system was demonstrated to be in accord with the real-time requirements of a driver fatigue detection system.

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Section: Brief Introduction Of Perclosmentioning

confidence: 99%

Driver fatigue detection based on eye state

Lin¹,

Huang²,

Ni³

et al. 2015

THC

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show abstract

“…This is particularly useful in vision-based navigation systems as all the images, provided by the camera system, contain the same information, independent of the rotation of the robot in the direction of the optical axis of the camera. This makes the computed image features more suitable for localization and navigation purposes (Hrabar & Sukhatme, 2003;Hampton et al, 2004). The methods proposed have been developed for vision-based navigation of Autonomous Ground Vehicles which utilize an omni-directional camera system as the vision sensor.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Omnidirectional Vision Localization Using a Beacon Tracker Based on Particle Filter

Cao¹,

Meng²,

Liu³

2008

Computer Vision

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“…In our experiment, we use Hough algorithm to recognize the landmark at the first frame as the prior probability value. The Hough transform has been widely used to detect patterns, especially those well parameterized patterns such as lines, circles, and ellipses (Guo et al, 2006). Here we utilize DSP processor which has high speed than PC to perform the Circular Hough Transform.…”

Section: Beacon Recognitionmentioning

confidence: 99%