An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

A Traffic Prediction for Intelligent Transportation System using Machine Learning

Traffic control has been an issue for a long time from the past. The modern world demands Technology. Now a days cars are one of the main methods of improvement in technology. Intelligent Traffic System is also known as Intelligent Transportation System apply communication and information technology to find the solution for the Traffic control issues. Intelligent Transportation System represents the main problem in transportation. ITS is a program .it is used to improve the efficiency of transportation through advanced technologies by using sensors and communication. Some of the problems like Traffic congestion, Low safety can be solved through this Intelligent Transportation System by Using the Latest techniques in traffic management.ITS is improved by using wireless and wireline communication-based information, control and electronic technologies. Now a days overspeeding is a key issue in the traffic control system to overrule the issue. Dophler Phenomenon is used for speed measurement.

An Efficient Pairing-free Batch Verification Scheme for VANET

An intelligent traffic system, which can flexibly allocate traffic resources, serves as a good assistant to help us improve traffic safety and efficiency of controlling traffic volume, providing instant traffic information and giving priority to ambulances. Although such system is powerful, it could be misused without prop er protection. For example, malicious drivers might forge the message of the ambulance so that they can quickly pass through intersections. In addition, because traffic information is huge and needs to be processed immediately, traditional schemes that pro cess the information one by one are not competent. For this issue, a lot of batch schemes have been proposed. Most of them adopt the algorithm of Bilinear pairing while the others tries to avoid it since pairing operations are complex. However, such pairin g - free schemes are not applicable because their calculation time will explode when there are more data waiting to be processed. In this article, we briefly describe those schemes and propose a more effective one to solve the problems mentioned above.

CITY-SCALE TAXI DEMAND PREDICTION USING MULTISOURCE URBAN GEOSPATIAL DATA

Real-time, accurate taxi demand prediction plays an important role in intelligent traffic system. It can help manage taxi patching and minimize the time and energy waste caused by waiting. In the era of big data, a diversity of urban data and increasingly complex traffic data have been collected and published. Traditional forecasting methods have been unable to cope with the heterogeneous massive traffic data, whereas deep learning, as a new data-oriented technique, has been widely used in the field of traffic prediction. This paper aims to utilize multisource data and deep learning techniques to improve the accuracy of taxi demand prediction. In this paper, a joint guidance residual network JG-Net is proposed for city-scale taxi demand prediction. Taxi order data and multiple urban geospatial data POI, road network and population distribution data) are integrated into the JG-Net. Regional features are considered in the prediction process by three guidance branches composed of pixel-adaptive convolutional networks, each of which applies one type of urban data. JG-Net assigns learnable weights to different branches and regions to combine the output of the branches, then further aggregates weather and time information to forecast the taxi demand. Extensive experiments and analyses are conducted, which show our method outperforms traditional methods. The mean square error of the prediction result on the testing set is 1.868, which is 12.3% lower than related models. The positive influence of combining multiple geospatial data is also validated by ablation experiments.