Traffic congestions happen more and more frequently on the current urban roads. Detecting the congestion rapidly and effectively can avoid the second damages. In this paper, we use the traffic images as data source instead of the videos to detect traffic congestions, which have the advantages of low cost and big probability to be applied widely. Firstly, the interest region of the traffic images are calibrated manually, and then the image features in the interest region are abstracted, including the sift corner, gray histogram variance, gray level co-occurrence matrix of energy and contrast. Finally, BP neural network is used to realize image multi-feature fusion, and to classify the traffic condition described by the traffic images. The simulation results show that the method can recognize the traffic condition with the accuracy of 95%.
Obtaining 3D information of vehicles as the basis for accurate classification of vehicles has become an increasingly important research direction. However, most of the current traffic monitoring cameras are monocular cameras, which cannot directly obtain 3D information of vehicles like pose and size due to perspective factors. According to the above problem, this paper proposes a 3D vehicle information recognition algorithm of monocular camera based on self-calibration in traffic scene. Firstly, this paper builds up a monocular camera model and a stable single vanishing point calibration model according to the typical traffic scene, and completes camera calibration. Then it uses the YOLO deep learning convolution neural network for 2D vehicle detection. Based on this, it puts forward a diagonal and vanishing point constrained non-linear optimization algorithm, combining with the calibration information to complete 3D vehicle information recognition and the best 3D vehicle detection. Finally, the experiment was carried out on the public dataset called BrnoCompSpeed and in highway traffic scenes, and the results show that the algorithm can effectively complete 3D vehicle information recognition in various traffic scenarios with an average recognition accuracy of more than 90%.
Computer vision techniques have been widely applied in Intelligent Transportation Systems (ITSs) to automatically detect abnormal events and trigger alarms. In the last few years, many abnormal traffic events, such as illegal parking, abandoned objects, speeding, and overloading, have occurred on the highway, threatening traffic safety. In order to distinguish illegal parking and abandoned object events, we propose an effective method to classify these types of abnormal objects. First, abnormal areas are detected by feature point extraction and matching. The transformation relation, between the world and image coordinate systems, is then established by camera calibration. Next, different-height inverse projection planes (IPPs) are built to obtain the inverse projection maps (IPMs). Finally, the 3D information describing the abnormal objects is estimated and used to distinguish illegally parked vehicles and abandoned objects. Experimental results from traffic image sequences show that this method is effective in distinguishing illegal parking and abandoned objects, while its low computational cost satisfies the real-time requirements; furthermore, it can be used in vehicle classification.
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