Obstacle detection by evaluation of optical flow fields from image sequences
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Mind the gap: Developments in autonomous driving research and the sustainability challenge
2020, Journal of Cleaner ProductionCitation Excerpt :In 1985, a pioneering computer-vision system was built by the University of Maryland’s computer vision lab (Davis and Kushner, 1986) for AV’s road and road network navigation, wherein the image processing component along with the implementation of a set of algorithms were investigated. Inspired by this work, a cruise control system is created for AV guidance on the German highway system (Maurer et al., 1996) and image sequences techniques are implemented (Enkelmann, 1991; Suzuki et al., 1992), laying the foundations for AV detection and navigation studies. Equally important to road recognition, efficient obstacle detection on roads within a short time helps trigger appropriate reactions to road situations.
Optical flow modeling and computation: A survey
2015, Computer Vision and Image UnderstandingCitation Excerpt :Autonomous car driving has received particular attention in recent years [94,98,235]. Obstacle detection and avoidance are the main tasks investigated for general robot control in real environment exploiting optical flow [57,67,82]. Automated video surveillance is another growing research field requiring motion analysis.
Independent components of optical flow in a multiresolution image sequence
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