Extensive research is now underway in the field of traffic monitoring systems, particularly focusing on the automated detection of traffic accidents. Surveillance cameras that are connected to traffic control systems are being installed at a growing number of urban intersections. Computer vision approaches have the potential to be very valuable tools for automatically detecting accidents. This study aims to provide an innovative and efficient framework for traffic monitoring applications, specifically focusing on detecting accidents occurring at crossings. The proposed framework starts with the first hierarchical phase, which involves the use of a Kalman filter and the Hungarian algorithm for object tracking and association. The second phase is referred to as trajectory conflict analysis, the third step is known as the advanced YOLOv4 technique, and the final stage is named efficient and accurate object detection. Subsequently, the procedure is completed. During the object tracking stage, the presence of obstructions, overlapping objects, and changes in shape are all carefully considered. This is achieved by using a distinct cost function. Analyzing object trajectories involves considering characteristics such as speed, angle, and distance in order to identify various forms of trajectory conflicts. Examples of these disputes include those pertaining to autos, individuals, and bicycles. These examples represent just a small fraction of the many forms of trajectory conflicts that may be detected. The experiment's results, based on video footage obtained from genuine traffic scenarios, indicate that the recommended approach has potential for use in real-time traffic monitoring technologies. The tests include evaluating various lighting conditions and video sequences that are imported from YouTube

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This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.

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