BodyCam-VQA: Enhanced Body-Worn Camera Video Captioning via Multimodal Reasoning and Probe Question Generation

A new arXiv paper (2609.10815v1) proposes BodyCam-VQA, an adaptive visual question answering framework designed to extract fine-grained forensic evidence from police body-worn camera footage that current vision-language models miss. The framework uses structured reasoning and multiple question-generation models, including foundation models and fine-tuned open-weight models, to produce more reliable and objective records of enforcement events. The authors say the approach aims to support legal transparency, officer accountability, and civilian and officer safety.

arXiv:2609.10815v1 Announce Type: new Abstract: Police body-worn camera BWC footage has emerged as a critical aspect of law enforcement that ensures legal transparency, officer accountability, and the protection of civil rights. However, effectively processing this data remains a significant challenge due to its multimodal video format. BWC videos, in many cases, comprise chaotic scenes with low visual quality, rapid movement/interactions, and high-noise audio that make visual understanding a challenge for even SOTA multimodal models. Current Vision-Language Models VLMs frequently overlook critical forensic details, such as the presence of valuable evidence or the latent nuances of suspect-officer interactions, which are vital for fair legal outcomes and civilian/officer safety. To address these limitations, we propose an Adaptive Visual Question Answering VQA framework engineered for high-stakes law enforcement. Our framework employs a structured reasoning approach to extract fine-grained visual evidence that traditional captioning systems fail to capture. We experiment with multiple question generation models, including foundation models and fine-tuned open-weight models, to observe performance variation among question generation model implementations. Our results demonstrate that this VQA-driven architecture provides a more reliable, objective, and detailed record of enforcement events, ultimately serving as a powerful tool to protect both law enforcement officers and the public through AI-assisted forensic clarity.