Pedestrian Movement Direction Recognition Using Convolutional Neural Networks

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/74053
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Title: Pedestrian Movement Direction Recognition Using Convolutional Neural Networks
Authors: Dominguez-Sanchez, Alex | Cazorla, Miguel | Orts-Escolano, Sergio
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Pedestrian detection | Advance driver assistance system | Convolutional neural networks | Pedestrian intention recognition
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: Dec-2017
Publisher: IEEE
Citation: IEEE Transactions on Intelligent Transportation Systems. 2017, 18(12): 3540-3548. doi:10.1109/TITS.2017.2726140
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-based technique that leverages current pedestrian detection techniques (histograms of oriented gradients-linSVM) to generate a sum of subtracted frames (flow estimation around the detected pedestrian), which are used as an input for the proposed modified versions of various state-of-the-art CNN networks, such as AlexNet, GoogleNet, and ResNet. Moreover, we have also created a new data set for this purpose, and analyzed the importance of training in a known data set for the neural networks to achieve reliable results.
Sponsor: This work was supported by the Feder funds, Spanish Government through the COMBAHO Project, under Grant TIN2016-76515-R, and in part by the University of Alicante Project under Grant GRE16-19.
URI: http://hdl.handle.net/10045/74053
ISSN: 1524-9050 (Print) | 1558-0016 (Online)
DOI: 10.1109/TITS.2017.2726140
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 IEEE
Peer Review: si
Publisher version: http://dx.doi.org/10.1109/TITS.2017.2726140
Appears in Collections:INV - RoViT - Artículos de Revistas

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