Abstract
This paper introduces a novel smartphone-based solution to detect different traffic rule violations using a variety of computer vision and networking technologies. We propose the use of smartphones as participatory sensors via their cameras to detect the moving and stationary objects (e.g., cars and lane markers) and understand the resulting driving and traffic violation of each object. We propose novel framework which uses a fast in-mobile traffic violation detector for rapid detection of traffic rule violation. After that, the smartphone transmits the data to the cloud where more powerful computer vision and machine learning operations are used to detect the traffic violation with a higher accuracy. We show that the proposed framework detection is very accurate by combining a) a Haar-like feature cascade detector at the in-mobile level, and b) a deep learning-based classifier, and support-vector machine-based classifiers in the cloud. The accuracy of the deep convolutional network is about 92% for true positive and 95% for true negative. The proposed framework demonstrates a potential for mobile-based traffic violation detection by especially by combining the information of accurate relative position and relative speed. Finally, we propose a real-time scheduling scheme in order to optimize the use of battery and real-time bandwidth of the users given partially known navigation information among the different users in the network, which us the real case. We show that the navigation information is very important in order to better utilize the battery and bandwidth for each user for a small number of users compared to the navigation trajectory length. That is, the utilization of the resources is directly related to the number of available participants, and the accuracy of navigation information.
Highlights
There are major challenges in transportation that require immediate innovative solutions [1]
We propose a novel smartphone-based participatory sensing system for traffic rule violation detection that is accommodating to new computer vision, sensing, and networking technologies
This paper propose a novel solution that provides accurate and low-cost traffic violation detection
Summary
There are major challenges in transportation that require immediate innovative solutions [1]. We propose a solution to detect the traffic rule violation of the other cars on the road, without the need to install any device in the monitored car, rather, we propose the use of smartphones as monitoring devices. The in-mobile component first detects the traffic rule violation on the roads using based on multiple subsequent time frames. This component is fast and had limited resources, and limited accuracy. We propose the relative positioning and relative speeding concept in out system design in order to infer the traffic violation from the computer vision algorithms and the smartphone sensors readings These two metrics allow us to understand the driving context from which the traffic rule violation can be detected.
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