Moving Forward with BWC: The Faleb Dataset for Multimodal Image Analysis

Body worn cameras (BWCs) have grown in popularity over the last decade. They are becoming one of the most essential tools used by law enforcement for surveillance. Limited academic research has been conducted on image and video processing using BWCs. The number of datasets based on BWCs is incredibly few. For this objective, we introduce FALEB (Face, Action, License, Egocentric look using Body worn cameras): a multimodal dataset for image processing using BWCs. This work includes two distinct insights: (1) introduction of a dataset specific to body cameras with the applications of facial recognition, action recognition, license plate recognition, and egocentric look, and (2) baseline experiments on the dataset. We investigate the methodologies employed in extracting meaningful patterns from BWC footage, the effectiveness of deep learning models in recognizing faces and cat-egorizing actions, and the potential applications of these advancements. By focusing on events uniquely relevant 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 complete dataset is available for research purposes and can be accessed by contacting the authors <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.