New Algorithm Improves Speed, Accuracy of Pedestrian Detection

“We’re aiming to build computer vision systems that will help computers better understand the world around them,” said Nuno Vasconcelos, electrical engineering professor at the UC San Diego Jacobs School of Engineering who directed the research. A big goal is real-time vision, he says, especially for pedestrian detection systems in self-driving cars. Vasconcelos is a faculty affiliate of the Center for Visual Computing and the Contextual Robotics Institute, both at UC San Diego. The new pedestrian detection algorithm developed by Vasconcelos and his team combines a traditional computer vision classification architecture, known as cascade detection, with deep learning models. Pedestrian detection systems typically break down an image into small windows that are processed by a classifier that signals the presence or absence of a pedestrian. This approach is challenging because…

Link to Full Article: New Algorithm Improves Speed, Accuracy of Pedestrian Detection

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