LOCALIZING FIREARM CARRIERS BY IDENTIFYING HUMAN-OBJECT PAIRS October 27, 2020 – Posted in: Uncategorized

Gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected.

Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human.A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not. Extensive experiments were performed to evaluate effectiveness of the algorithm, including exploiting full pose of the human, hand key-points, and their association with the firearm. The knowledge of spatially localized features is key to success of our method by using multi-size proposals with adaptive average pooling. We have also extended a previously firearm detection dataset, by adding more images and tagging in extended dataset the human-firearm pairs (including bounding boxes for firearms and gunmen). The experimental results (AP = 78.5) demonstrate effectiveness of the proposed method.

ABDUL BASIT, M. AKHTAR MUNIR, MOHSEN ALI AND ARIF MAHMOOD
ACCEPTED IN ICIP2020

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