In intelligent transportation systems, there are many tasks that rely on the detection of road congestion, such as traffic signal scheduling and traffic accident detection. As traditional methods for traffic congestion detection are difficult to use, expensive, and may cause damage to the road surface, this paper presents a method for road congestion detection that is based on multidimensional visual features and a convolutional neural network (CNN). This method first detects the density of foreground objects by using a gray-level co-occurrence matrix; second, the speed of moving objects is detected by using the Lucas-Kanade optical flow with pyramid implementation. Third, a Gaussian mixture model is used to model the background, and the CNN is then used to accurately detect the final foreground from the candidate foregrounds. Finally, the proposed method performs road congestion detection in terms of a multidimensional feature space, including traffic density, traffic velocity, road occupancy, and traffic flow. Furthermore, we propose an information entropy method using a histogram of optical flow to enhance the accuracy and reliability of road congestion detection. Simulation results via quantitative and qualitative assessment indicate that the proposed method is able to significantly outperform the state-of-the-art road-traffic congestion detection methods due to the fusion of multidimensional features using the CNN.

Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multi-scenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for the conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multi-scenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multi-scenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than the other state-of-the-art methods.

Recently, with the development of deep learning, Automatic License Plate Recognition (ALPR) has made great progress, However, there are still many challenges to accomplish license plate (LP) recognition under various traffic scenarios. One of them is the detection speed and recognition speed, and the other is the difficulty to recognize the low resolution and highly tilted LP images. In this paper, we present a two-stage ALPR framework to achieve efficient LP detection and recognition in unconstrained scenarios. Our LP detector is based on improved Yolov3-tiny, and we also propose a lightweight recognition network MRNet based on multi-scale features. In order to improve the inference speed, we abandoned the rectification of LP images, and RNNs that are difficult to compute in parallel. Additionally, we also propose a license plate data augmentation method, which achieves more effective augmentation and improves the generalization ability of the network through secondary random and hyperparametric search. As an additional contribution, we provide a challenging dataset collected from real-world driving recorders. The dataset is for multiple LPs in a single image, which makes up for the lack of public datasets in multiple LP scenarios. We evaluated the results on five datasets and showed that we achieved the best performance on almost all test sets, achieving 99.8% accuracy on CCPD with more than 180,000 license plate test sets. In terms of speed, the inference speed for detecting license plates reaches 751 FPS, and the fastest inference time for recognizing a single license plate takes only 2.9 ms.