  In the dynamic urban landscape, where the interplay of vehicles and
pedestrians defines the rhythm of life, integrating advanced technology for
safety and efficiency is increasingly crucial. This study delves into the
application of cutting-edge technological methods in smart cities, focusing on
enhancing public safety through improved traffic accident detection. Action
recognition plays a pivotal role in interpreting visual data and tracking
object motion such as human pose estimation in video sequences. The challenges
of action recognition include variability in rapid actions, limited dataset,
and environmental factors such as (Weather, Illumination, and Occlusions). In
this paper, we present a novel comprehensive dataset for traffic accident
detection. This datasets is specifically designed to bolster computer vision
and action recognition systems in predicting and detecting road traffic
accidents. We integrated datasets from wide variety of data sources, road
networks, weather conditions, and regions across the globe. This approach is
underpinned by empirical studies, aiming to contribute to the discourse on how
technology can enhance the quality of life in densely populated areas. This
research aims to bridge existing research gaps by introducing benchmark
datasets that leverage state-of-the-art algorithms tailored for traffic
accident detection in smart cities. These dataset is expected to advance
academic research and also enhance real-time accident detection applications,
contributing significantly to the evolution of smart urban environments. Our
study marks a pivotal step towards safer, more efficient smart cities,
harnessing the power of AI and machine learning to transform urban living.
