CVAT-BWV: A Web-Based Video Annotation Platform for Police Body-Worn Video
CVAT-BWV: A Web-Based Video Annotation Platform for Police Body-Worn Video
Parsa Hejabi, Akshay Kiran Padte, Preni Golazizian, Rajat Hebbar, Jackson Trager, Georgios Chochlakis, Aditya Kommineni, Ellie Graeden, Shrikanth Narayanan, Benjamin A.T. Graham, Morteza Dehghani
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8674-8678.
https://doi.org/10.24963/ijcai.2024/1006
We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This is primarily due to resource constraints, the sensitive nature of the data, which limits widespread access, and consequently, lack of annotations for training machine learning models. Our platform, called CVAT-BWV, offers a secure, locally hosted annotation environment that integrates several AI tools to assist in annotating multimodal data. With features such as automatic speech recognition, speaker diarization, object detection, and face recognition, CVAT-BWV aims to reduce the manual annotation workload, improve annotation quality, and allow for capturing perspectives from a diverse population of annotators. This tool aims to streamline the collection of annotations and the building of models, enhancing the use of BWV data for oversight and learning purposes to uncover insights into police-civilian interactions.
Keywords:
AI Ethics, Trust, Fairness: ETF: Societal impact of AI
Humans and AI: HAI: Human-AI collaboration
Machine Learning: ML: Applications
Computer Vision: CV: Applications