Video Surveillance of Highway Traffic Events by Deep Learning Architectures

TL;DR

This paper presents a deep learning-based video surveillance system for highway traffic event detection, comparing RNN and CNN architectures, with promising results for real-time deployment.

Contribution

It introduces and evaluates multiple deep learning architectures, including transfer learning, for detecting traffic events in highway videos.

Findings

RNN-based approach effectively analyzes temporal sequences.

CNN-RNN hybrid models improve detection accuracy.

Transfer learning enhances model performance.

Abstract

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.