Citation

Dominguez-Sanchez A, Cazorla MI, Orts-Escolano S. IEEE Trans. Intel. Transp. Syst. 2017; 18(12): 3540-3548.

Copyright

(Copyright © 2017, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2017.2726140

PMID

unavailable

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.

Language: en

Keywords

advance driver assistance system; Autonomous automobiles; autonomous cars; autonomous driver assistance; Biological neural networks; CNN networks; convolutional neural networks; Convolutional neural networks; crucial moving objects; deep learning techniques; driver information systems; fragile moving objects; Histograms; histograms of oriented gradients-linSVM; image recognition; learning (artificial intelligence); neural nets; object detection; Pedestrian detection; pedestrian detection techniques; pedestrian intention recognition; pedestrian movement direction recognition; pedestrians; security surveillance systems; support vector machines; traffic engineering computing; Training; Trajectory