Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR) algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D) information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti‐noise performance, over‐segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over‐segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti‐noise performance, highly precise segmentation result, and are useful in effectively segmenting low‐contrast images.

The combination of the 2-D spatial information of the scene and 1-D spectral information proves to be an effective method for improving classification accuracy. However, the classification accuracy will be greatly reduced when the trained classification models are applied to arbitrary cropped hyperspectral images (HSIs). In an attempt to the above-mentioned problem, dual-channel networks with optimal-band selection (DNOS) is proposed. DNOS is composed of bidirectional recurrent neural network (RNN) branch for spectral features and multiscale convolutional neural network (CNN)-3D branch for spatial features. Besides, the optimal-band selection strategy is applied to distinguish characteristic bands of different ground objects. The spatial information is obtained via the texture feature method based on the optimal-band selection strategy. Texture-extracted spatial patches are sent to CNN-3D branch. Meanwhile, the spectrum is sent into the bidirectional RNN branch which can strengthen bands most relevant to ground object. The public hyperspectral data set shows that the DNOS model outperforms the other methods, especially in arbitrary cropped HSIs. Overall accuracy (OA), average accuracy (AA), and Kappa of the DNOS model are at least 8.3%, 8.3%, and 10.2% than comparison methods in the letter.

Abandoned objects in a road can be a potential threat to urban traffic. In this article we present a new threat degree analysis method of abandoned objects, which is based on dynamic multifeature fusion to detect the abandoned objects and analyze the threat degree automatically. The image sequence is acquired with a visible sensor installed at overbridges, and is processed by a PC-based image-processing system. In this method, we first put forward a multivehicle tracking algorithm to acquire the vehicles' moving information as preprocessing. Then we extracted the dynamic features and built a security threat model upstream of the abandoned object to facilitate further analysis. Finally, we proposed a fusion decision method, based on dynamic multiple features, to analyze the threat degree. The experiments show that the developed method can determine the threat degree of abandoned objects well and truly, and it is appropriate in the intelligent traffic monitoring system.