Vision based real time traffic monitoring

A system and method for detecting and tracking one or more vehicles using a system for obtaining two-dimensional visual data depicting traffic flow on a road is disclosed. In one exemplary embodiment, the system and method identifies groups of features for determining traffic data. The features are classified as stable features or unstable features based on whether each feature is on the frontal face of a vehicle close to the road plane. In another exemplary embodiment, the system and method identifies vehicle base fronts as a basis for determining traffic data. In yet another exemplary embodiment, the system and method includes an automatic calibration procedure based on identifying two vanishing points

Intelligent Video Ingestion for Real-time Traffic Monitoring

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordAs an indispensable part of modern critical infrastructures, cameras deployed at strategic places and prime junctions in an intelligent transportation system (ITS), can help operators in observing traffic flow, identifying any emergency situation, or making decisions regarding road congestion without arriving on the scene. However, these cameras are usually equipped with heterogeneous and turbulent networks, making the realtime smooth playback of traffic monitoring videos with high quality a grand challenge. In this paper, we propose a light-weight Deep Reinforcement Learning (DRL) based approach, namely sRC-C (smart bitRate Control with a Continuous action space), to enhance the quality of realtime traffic monitoring by adjusting the video bitrate adaptively. Distinguished from the existing bitrate adjusting approaches, sRC-C can overcome the bias incurred by deterministic discretization of candidate bitrates by adjusting the video bitrate with more f ine-grained control from a continuous action space, thus significantly improving the Quality-of-Service (QoS). With carefully designed state space and neural network model, sRC-C can be implemented on cameras with scarce resources to support real-time live video streaming with low inference time. Extensive experiments show that sRC-C can reduce the frame loss counts and hold time by 24% and 15.5%, respectively, even with comparable bandwidth utilization. Meanwhile, compared to the-state-of-art approaches, sRC-C can improve the QoS by 30.4%.National Key Research and Development Program of ChinaEuropean Union Horizon 2020Leading Technology of Jiangsu Basic Research PlanNational Natural Science Foundation of ChinaChongqing Key Laboratory of Digital Cinema Art Theory and Technolog

Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8

Computer vision applications are important nowadays because they provide solutions to critical problems that relate to traffic in a cost-effective manner to reduce accidents and preserve lives. This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art you-only-look-once v8 algorithm, benefiting from its functionalities to provide vehicle detection, classification, and segmentation. The proposed work provides various important traffic information, including vehicle counting, classification, speed estimation, and size estimation. This information helps enforce traffic laws. The proposed system consists of five stages: The preprocessing stage, which includes camera calibration, ROI calculation, and preparing the source video input; the vehicle detection stage, which uses the convolutional neural network model to localize vehicles in the video frames; the tracking stage, which uses the ByteTrack algorithm to track the detected vehicles; the speed estimation stage, which estimates the speed for the tracked vehicles; and the size estimation stage, which estimates the vehicle size. The results of the proposed system running on the Nvidia GTX 1070 GPU show that the detection and tracking stages have an average accuracy of 96.58% with an average error of 3.42%, the vehicle counting stage has an average accuracy of 97.54% with a 2.46% average error, the speed estimation stage has an average accuracy of 96.75% with a 3.25% average error, and the size estimation stage has an average accuracy of 87.28% with a 12.72% average error

Database Integration Model for Automatic Identification System and Shipping Database In Real Time Traffic Monitoring

In recent years, there are so many marine accidents in the world such as ship sinking (37%), grounding (13%), collisions (15%), fire (18%) and other types of accidents (17%). While the causes of the ship accident are 37% of human error, technical error of 23%, 38% due to natural conditions, and 2% to other causes. One of the causes is the weakness of the identification and monitoring the ships passing through the shipping channel in Indonesia. Automatic Identification System (AIS) on the previous research has been carried out with the integration of Geographic Information System (GIS) that allows measuring the use of AIS for fuels monitoring and the development of ship inspection priority system based on the level of risk held by each vessel. Those studies are expected to minimize the level of accidents that occurred in Indonesian waters. However, the result of previous studies showed that the identity of the ship is still conducted separately. It is thus necessary to perform data integration with databases AIS vessel into a database server. This research is focused on the development of the integration between AIS data and shipping database, therefore, It could be used as the backbone of integration system for measuring safety and pollution of the ships, such as basis for monitoring traffic analysis, estimates of air pollution, vessel inspections in real time and direct search vessel identity