Authors:
Sameer Hans
1
;
Jean-Luc Dugelay
1
;
Mohd Rizal Mohd Isa
2
and
Mohammad Adib Khairuddin
2
Affiliations:
1
EURECOM, 450 Route des Chappes, 06410 Biot, France
;
2
Universiti Pertahanan Nasional Malaysia (UPNM), Kem Perdana Sg. Besi, 57000 Kuala Lumpur, Malaysia
Keyword(s):
Body Worn Camera, Multimodal Dataset, Action Recognition, Surveillance.
Abstract:
Over the past decade, there has been a notable increase in the integration of body worn cameras (BWCs) in many professional settings, particularly in law enforcement. BWCs serve as valuable tools for enhancing transparency, accountability, and security by providing real-time, first-person perspective recordings of interactions and events. These devices capture vast amounts of video data, which can offer critical insights into the behaviors and actions of individuals in diverse scenarios. This paper aims to explore the intersection of BWCs and action recognition methodologies. We introduce FALEBaction: a multimodal dataset for action recognition using body worn cameras, with actions relevant to BWCs and law enforcement usage. We investigate the methodologies employed in extracting meaningful patterns from BWC footage, the effectiveness of deep learning models in recognizing similar actions, and the potential applications and implications of these advancements. By focusing on actions r
elevant to law enforcement scenarios, we ensure that our dataset meets the practical needs of the authorities and researchers aiming to enhance public safety through advanced video analysis technologies. The entire dataset can be obtained upon request from the authors to facilitate further research in this domain.
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