Search

Multiple vehicles tracking in the aerial video have a strong effect on the intelligent transportation system. Therefore, a fast multiple vehicles tracking method is required to meet the demand of a higher energy conversion efficiency. However, frequent and long-term occlusions in complex traffic scenes make it more difficult, and identity switches of vehicles with a similar appearance also bring challenges. Currently, vehicles tracking methods based on detection shows satisfactory performance. In the tracking-after-detection methods, data association plays the key role, and it includes two types, namely, frame-by-frame association and multi-frame association. Frame-by-frame association refers to the association between detections in the two consecutive frames, but tracking drift or failure is likely to occur when vehicles are blocked or not detected. The multi-frame association establishes a relational model by using object detection information of multiple frames instead of the previous two frames, which can effectively reduce the vehicle error association and deal with occlusions. However, the tracking will also be interrupted if the occlusion time is too long to associate the detection points before and after. Therefore, online multiple vehicles tracking in the aerial video based on fast incremental discriminative appearance learning is put forward. A fast incremental discriminative appearance learning (FIDAL) is introduced to discriminate the appearances of vehicles and adaptively update the vehicle appearance models based on the difference value between the new sample and the mean of vehicle samples to address the problem of identity switches. Experimental results on video sequences from different data sets demonstrate an average 25 percent performance improvement when using fast incremental discriminative appearance learning.

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle (UAV) is more and more utilized to capture the aerial images. Therein, the registration of the aerial infrared images play a vital role in the infrared image processing, such as object tracking, information collecting, and behavior analysis. However, due to the low contrast, low-resolution, and few texture features of the infrared images, it is really difficult to implement the image registration in the aerial infrared images. In our work, an accurate and efficient image registration algorithm in the aerial infrared images is put forward. To improve the registration accuracy and efficiency, we construct multiple screening mechanisms to screen out the incorrect and redundant feature points. Besides, to further improve the registration accuracy, the improved SIFT and convolution neural network (CNN) descriptors are combined for the feature description. Finally, the coarse-to-fine feature matching strategy is put forward via FLANN and RANSAC algorithms. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive registration results.

Due to the limited band width and congestion of communication channels in the wireless vehicle-to-vehicle (V2V) communication, time delay inevitably arises and dramatically leads to the disturbances for the automated vehicular platoon. This paper focuses on computing the exact time delay stability margin. In this study, we treat this problem as a stability issue of a consensus system with time delay, where each vehicle in the platoon is recognized as a node, and the interconnected information flow is represented as a graph. Then, the distributed controller is designed by combining the states of the vehicle itself and its neighbouring vehicles. Furthermore, the stability of the entire platoon is analysed according to the characteristic equation of the closed-loop system, and a necessary and sufficient condition for the exact time delay stability margin is obtained. Especially, for the automated vehicular platoon with undirected topology, it is revealed that exact time delay stability margin is determined by the largest eigenvalue of the augmented Laplacian matrix. Furthermore, a rapid method for finding exact time delay stability margin is proposed. Finally, numerical simulations demonstrate that this work generates exact and satisfactory time delay stability margin for the automated vehicular platoon.

Vehicle detection in aerial images has attracted great attention as an approach to providing the necessary information for transportation road network planning and traffic management. However, because of the low resolution, complex scene, occlusion, shadows, and high requirement for detection efficiency, implementing vehicle detection in aerial images is challenging. Therefore, we propose an efficient and scene-adaptive algorithm for vehicle detection in aerial images using an improved YOLOv3 framework, and it is applied to not only aerial still images but also videos composed of consecutive frame images. First, rather than directly using the traditional YOLOv3 network, we construct a new structure with fewer layers to improve the detection efficiency. Then, since complex scenes in aerial images can cause the partial occlusion of vehicles, we construct a context-aware-based feature map fusion to make full use of the information in the adjacent frames and accurately detect partially occluded vehicles. The traditional YOLOv3 network adopts a horizontal bounding box, which can attain the expected detection effects only for vehicles with small length&ndash, width ratio. Moreover, vehicles that are close to each other are liable to cause lower accuracy and a higher detection error rate. Hence, we design a sloping bounding box attached to the angle of the target vehicles. This modification is conducive to predicting not only the position but also the angle. Finally, two data sets were used to perform extensive experiments and comparisons. The results show that the proposed algorithm generates the desired and excellent performance.

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more adopted to capture the aerial images. Therefore, how to fully utilize the aerial infrared images for the vehicle detection has attracted widespread attentions. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is really difficult to implement the vehicle detection in the aerial infrared images. In our work, an efficient and accurate vehicle detection algorithm in aerial infrared images is proposed via an improved yolov3 network. To increase the detection efficiency, we construct a new structure of the improved yolov3 network with only 16 layers. Besides, we expand the anchor boxes to four scales to improve the detection accuracy of the small vehicles. Meanwhile, for the limitation of the infrared vehicle samples, the transfer learning is introduced to train the improved yolov3 network. Finally, the proposed algorithm is evaluated on the VIVID and NPU data sets. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive vehicle detection results.

Multiple vehicle tracking in aerial video has vital applications in intelligent transportation system. However, this is challenging owing to the demand for higher tracking efficiency. Moreover, it is particularly challenging to handle shadows and occlusions in complex traffic scenes. Currently, most approaches mainly address the multiple vehicle tracking problem based on consecutive frame pairs. However, they are inefficient and ineffective with respect to the tracking of vehicles that are shadowed or occluded by trees, buildings, or large vehicles. Therefore, in terms of consecutive multiframe images, we propose an efficient deshadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching. By processing a series of frames at a time, this approach will address the occurrence of shadows and occlusions in multiple vehicle tracking. First, image segmentation and road segmentation are combined to construct the region adjacency graph within the road (RAGR) for each frame. Local region matching is then performed among the RAGRs based on the distance transform. In particular, to improve the region matching efficiency, a local matching criterion is proposed by introducing region of interest for the node of the RAGR. Finally, based on the motion dissimilarities and varying spatial distances of the background and moving vehicles, a batch-oriented foreground separation is achieved to separate vehicles (the foreground) from the background. Experiments and comparative analysis conducted on UAV123 and DARPA VIVID data sets demonstrate that the proposed approach for multiple vehicle tracking in aerial video can generate satisfactory and competitive results.

Vehicle detection in high-resolution aerial images has received widespread interests when it comes to providing the required information for traffic management and urban planning. It is challenging due to the relatively small size of the vehicles and the complex background. Furthermore, it is particularly challenging if the higher detection efficiency is required. Therefore, an urban vehicle detection algorithm is proposed via improved entropy rate clustering (IERC) and correlation-based sequential dictionary learning (CSDL). First, to enhance the detection accuracy, IERC is designed to generate more regular superpixels. It aims to avoid the situation that one superpixel sometimes straddles multiple vehicles. The generated superpixels are then treated as the seeds for the training sample selection. Then, CSDL is constructed to achieve a fast sequential training and updating of the dictionary. In CSDL, only the atoms correlated with the sparse representation of the new training data are inferred. Finally, comprehensive analyses and comparisons on two data sets demonstrate that the proposed method generates satisfactory and competitive results.