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We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.

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The Dataset consists of two videos, one recorded with blindfold on and the other without blindfold recorded using a 1080p Intel RealSense depth camera. It contains the videos, images extracted using ffmpeg and processed video which is made of a video with skipped frames created using ffmpeg. The scope of the dataset is for machine vision purposes to allow for tasks such as instance segmentation. A hat fixed on the head of a blindfolded person is used to record walking activities.

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