Towards Safer Transportation: a self-supervised learning approach for traffic video deraining
Video monitoring of traffic is useful for traffic management and control, traffic counting, and traffic law enforcement. However, traffic monitoring during inclement weather such as rain is a challenging task because video quality is corrupted by streaks of falling rain on the video image, and this hinders reliable characterization not only of the road environment but also of road-user behavior during such adverse weather events. This study proposes a two-stage self-supervised learning method to remove rain streaks in traffic videos. The first and second stages address intra- and inter-frame noise, respectively. The results indicated that the model exhibits satisfactory performance in terms of the image visual quality and the Peak Signal-Noise Ratio value.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementRain RemovalSelf-Supervised LearningVideo derainingSimilar Papers 제목 키워드 기반
SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation rout…
Cloud ComputingThe 4th AI City Challenge
The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from da…
Anomaly DetectionComputational EfficiencyVehicle Re-IdentificationEnhanced Urban Traffic Management Using CCTV Surveillance Videos and Multi-Source Data Current State Prediction and Frequent Episode Mining
Rapid urbanization has intensified traffic congestion, environmental strain, and inefficiencies in transportation systems, creating an urgent need for intelligent and adaptive traffic management solutions. Conventional s…
Traffic PredictionTrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Traffic flow analysis is revolutionising traffic management. Qualifying traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest routes and therefore optimising transpo…
Instance SegmentationManagementSemantic SegmentationConnected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting the motion of surrounding agents and pla…
Autonomous VehiclesMotion Planning