Constructing Datasets From Public Police Body Camera Footage

The enormous potential of body-worn cameras to improve accountability in policing remains largely unrealized due to large volumes of unreviewed footage. Transcription and diarization tools could aid in reviewing footage, but lack of public data hinders their development. We develop a pipeline to construct public datasets, making use of the small number of videos publicly released by police departments, with capacity to update the data as footage gets released or removed. Our pipeline produces two datasets, a large one with transcriptions automatically extracted from department-generated captions, and a smaller test set where we manually validated transcripts and alignment. We benchmark ASR models, including models fine-tuned on our data, on our test set, to show applications of our datasets and continued challenges of this domain. Our work presents a new vision for leveraging public body-worn camera footage—even when it can’t be rereleased—to help address this critical social issue.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888621
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

Constructing Datasets From Public Police Body Camera Footage

Karen Livescu, Anjalie Field, Martijn Bartelds, Leibny Paola Garcia et al.
AI in cancer detection
article

Constructing Datasets From Public Police Body Camera Footage

Karen Livescu, Anjalie Field, Martijn Bartelds, Leibny Paola Garcia, Dan Jurafsky, Ruizhe Huang, Jamie Rosas-Smith
article en

Abstract

The enormous potential of body-worn cameras to improve accountability in policing remains largely unrealized due to large volumes of unreviewed footage. Transcription and diarization tools could aid in reviewing footage, but lack of public data hinders their development. We develop a pipeline to construct public datasets, making use of the small number of videos publicly released by police departments, with capacity to update the data as footage gets released or removed. Our pipeline produces two datasets, a large one with transcriptions automatically extracted from department-generated captions, and a smaller test set where we manually validated transcripts and alignment. We benchmark ASR models, including models fine-tuned on our data, on our test set, to show applications of our datasets and continued challenges of this domain. Our work presents a new vision for leveraging public body-worn camera footage—even when it can’t be rereleased—to help address this critical social issue.

Johns Hopkins University (US), Toyota Technological Institute at Chicago (US), Stanford University (US)
Openalex Percentile: Top 98%
AI in cancer detection
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