In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specific sequence of situations that occur in the video, as for instance a vehicle stopping on the emergency lane. Hence, the detection of these events requires to analyze a temporal sequence in the video stream. We compare different approaches that exploit architectures based on Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). A first approach extracts vectors of features, mostly related to motion, from each video frame and exploits a RNN fed with the resulting sequence of vectors. The other approaches are based directly on the sequence of frames, that are eventually enriched with pixel-wise motion information. The obtained stream is processed by an architecture that stacks a CNN and a RNN, and we also investigate a transfer-learning-based model. The results are very promising and the best architecture will be tested online in real operative conditions.
|Titolo:||Video surveillance of highway traffic events by deep learning architectures|
|Citazione:||Tiezzi, M., Melacci, S., Maggini, M., & Frosini, A. (2018). Video surveillance of highway traffic events by deep learning architectures. In Artificial Neural Networks and Machine Learning – ICANN 2018 (pp.584-593). Berlin : Springer Verlag.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|