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Pedestrian lane detection is a crucial task in assistive navigation for vision-impaired people. It can provide information on walkable regions, help blind people stay on the pedestrian lane, and assist with obstacle detection. An accurate and real-time lane detection algorithm can improve travel safety and efficiency for the visually impaired. Despite its importance, pedestrian lane detection in unstructured scenes for assistive navigation has not attracted sufficient attention in the research community. This paper aims to provide a comprehensive review and an experimental evaluation of methods that can be applied for pedestrian lane detection, thereby laying a foundation for future research in this area. Our study covers traditional and deep learning methods for pedestrian lane detection, general road detection, and general semantic segmentation. We also perform an experimental evaluation of the representative methods on a large benchmark dataset that is specifically created for pedes ....
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BENGALURU: A new study from IISc’s Centre for Neuroscience (CNS) has explored how well deep neural networks machine learning systems inspired by the network of brain cells or neurons in the human brain compare to the human brain when it comes to visual perception. Pointing out that deep neural networks can be trained to perform specific tasks, researchers say that they have played a pivotal role in helping scientists understand how our brains perceive the things that we see. “Although deep networks have evolved significantly over the past decade, they are still nowhere close to performing as well as the human brain in perceiving visual cues. In a recent study, SP Arun, associate professor at CNS, and his team have compared various qualitative properties of these deep networks with those of the human brain,” IISc said in a statement. ....