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Open Access Traffic Light Recognition and Dangerous Driving Events Detection from Surveillance Video of Vehicle Camera

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Surveillance video of vehicle camera are widely used to support driver's safe driving, especially for taxi and truck drivers. Long-term videos are often inspected by human operators manually to find dangerous driving events, which is tedious time consuming work. We propose a new method to detect dangerous driving events automatically from the surveillance videos. Events such as rocket start, red light ignored dangerous driving can be detected. In our method, traffic light recognition is made at first. Then speed and acceleration data of car, traffic light recognition results are used as features to detect dangerous driving events.

Color and shape of traffic lights are different in different countries and areas. Color and shape of traffic light images are also different at different shooting time, background and weather condition. It is difficult for conventional method to obtain both high recognition rate and low false positive rate. We proposed to use color, shape and context features to recognize traffic light more accurately.

Vehicle road testing in both USA and Japan were made to demonstrate effectiveness of our proposed method. Real-time processing recognition experiments were made by vehicle camera video stream. Surveillance videos taken by driving recorder camera were also used to do traffic light recognition and dangerous driving events detection experiments. Traffic light recognition rate of 93%, false positive detection rate of 0.1%, realtime processing time less than 30ms results were obtained by our method.

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Keywords: DANGEROUS DRIVING EVENT DETECTION; RED LIGHT IGNORED; ROCKET START; SPEED AND ACCELERATION OF CAR; SURVEILLANCE VIDEO OF VEHICLE CAMERA; TRAFFIC LIGHT COLOR RECOGNITION; TRAFFIC LIGHT RECOGNITION; TRAFFIC LIGHT SHAPE RECOGNITION

Document Type: Research Article

Publication date: January 29, 2017

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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