Abstract

According to worldwide statistics, traffic accidents are the cause of a high percentage of violent deaths. The time taken to send the medical response to the accident site is largely affected by the human factor and correlates with survival probability. Due to this and the wide use of video surveillance and intelligent traffic systems, an automated traffic accident detection approach becomes desirable for computer vision researchers. Nowadays, Deep Learning (DL)-based approaches have shown high performance in computer vision tasks that involve a complex features relationship. Therefore, this work develops an automated DL-based method capable of detecting traffic accidents on video. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure.

Highlights

  • There are different factors that cause traffic accidents

  • Deep learning neural networks architectures trained to detect the occurrence of a traffic accident are used

  • We assume that the model can operate correctly in the most popular devices used for vehicular traffic systems

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Summary

Introduction

There are different factors that cause traffic accidents. Among the most common factors that increase the probability of their occurrence are the geometry of the road [1], the climate of the area [2], drunk drivers, and speeding [3,4]. The group is based on methods related to machine learning and statistics [7,9,39,40] Many of these have presented solutions using artificial neural networks [41,42,43], support vector machines [16,44], probabilistic neural networks [45], autoencoders [46], block clustering [47], Random Forest [15], pattern recognition [18], image processing techniques [48] and the Hidden Markov Model [49], among others. These approaches perform well, and are able to partially deal with an unbalanced dataset

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