publication . Article . 2017

pedestrian movement direction recognition using convolutional neural networks

Alex Dominguez-Sanchez; Miguel Cazorla; Sergio Orts-Escolano;
Open Access
  • Published: 01 Dec 2017 Journal: IEEE Transactions on Intelligent Transportation Systems, volume 18, pages 3,540-3,548 (issn: 1524-9050, eissn: 1558-0016, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • Country: Spain
Abstract
Pedestrian movement direction recognition is an important factor in autonomous driver assistance and security surveillance systems. Pedestrians are the most crucial and fragile moving objects in streets, roads, and events, where thousands of people may gather on a regular basis. People flow analysis on zebra crossings and in shopping centers or events such as demonstrations are a key element to improve safety and to enable autonomous cars to drive in real life environments. This paper focuses on deep learning techniques such as convolutional neural networks (CNN) to achieve a reliable detection of pedestrians moving in a particular direction. We propose a CNN-ba...
Subjects
free text keywords: Advance driver assistance system, Convolutional neural networks, Pedestrian intention recognition, Ciencia de la Computación e Inteligencia Artificial, Flow estimation, Pedestrian detection, Simulation, Pedestrian, Deep learning, Histogram, Artificial intelligence, business.industry, business, Convolutional neural network, Artificial neural network, Engineering, Trajectory, Machine learning, computer.software_genre, computer, Computer vision
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