A Novel Storing and Accessing Method of Traffic Incident Video Based on Spatial-Temporal Analysis

Zhang, Yaying; Zhu, Y

doi:10.1007/978-3-319-27122-4_22

Cited by 1 publication

(1 citation statement)

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“…The last group is oriented to the amount of data collected by all the technology currently available in the community. Among them can be found data from social networks [12,17,50,51], data collected from sensors of a smartphone [13], structured data [52,53], data detected from video cameras [14,54,55], and traffic sensor data [56]. These datasets are widely used by deep learning, hybrid, and extreme learning methods.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Traffic Accidents from Video Using Deep Learning Techniques

2021

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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.

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