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 commercial centres 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 good and reliable detection of pedestrians moving in a particular direction. We present a novel input representation that leverages current pedestrian detection techniques to generate a sum of subtracted frames, which are used as an input for the proposed CNN. Moreover, we have also created a new dataset for this purpose.
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Acknowledgment
This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. Experiments were made possible by a generous hardware donation from NVIDIA.
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Dominguez-Sanchez, A., Orts-Escolano, S., Cazorla, M. (2017). Recognizing Pedestrian Direction Using Convolutional Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_21
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