Comprehensive urban navigation and yielding: video dataset for enhanced collision and anomaly detection in real-world traffic scenarios

Laura Kaplan; Vladimir Frants; Sos Agaian

doi:10.1117/12.3020191

7 June 2024 Comprehensive urban navigation and yielding: video dataset for enhanced collision and anomaly detection in real-world traffic scenarios

Laura Kaplan, Vladimir Frants, Sos Agaian

Author Affiliations +

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330M (2024) https://doi.org/10.1117/12.3020191
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

To advance road safety through technological innovation, we present the Comprehensive Urban Navigation and Yielding (CUNY) Video Dataset (CVD), a pioneering collection aimed at enriching the analysis of roadway incidents using stationary camera footage. Derived from 1,013 YouTube videos, CVD is intricately annotated to discern between collision and non-collision scenarios, opening avenues for profound insights into various roadway incidents. CVD has been meticulously curated to overcome prevalent limitations in existing collision databases, boasting a comprehensive representation of environmental conditions, camera qualities, geographical diversity, and temporal variations. It is particularly well-suited for integration with existing road monitoring infrastructures, enabling optimization of emergency response, enhancement of traffic management, and overall improvement in road safety. By openly disseminating this dataset, we seek to address the scarcity of accessible, diverse, and authentic video data for collision analysis, contributing to advancements in the field of intelligent transportation systems and fostering safer road environments.

Citation Download Citation

Laura Kaplan, Vladimir Frants, and Sos Agaian "Comprehensive urban navigation and yielding: video dataset for enhanced collision and anomaly detection in real-world traffic scenarios", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330M (7 June 2024); https://doi.org/10.1117/12.3020191

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