Application of improved you only look once model in road traffic monitoring system

The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms

Trends and Open Research Issues in Intelligent Internet of Vehicles

The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights the key technologies of IoV, network architecture and comparison of IoV architecture models with focus on different communication models The most modern IoV enabling technologies are also highlighted, along with environmental scope of intelligent internet of vehicles. Finally, the paper has reviewed the open research issues of Intelligent IoV such as Poor Connectivity of on road vehicles and Stability, Hard delay constraints, High reliability requirements, high scalability, Security and privacy, etc. and related solutions