Localizing Firearm Carriers by Identifying Human-Object Pairs

• URL: http://arxiv.org/abs/2005.09329v2
• Date: Wed, 20 May 2020 09:49:30 GMT
• Title: Localizing Firearm Carriers by Identifying Human-Object Pairs
• Authors: Abdul Basit, Muhammad Akhtar Munir, Mohsen Ali, Arif Mahmood
• Abstract summary: 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.
• Score: 12.425090880770977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual identification of 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 ($AP_{hold} = 78.5$) demonstrate effectiveness of the proposed method.

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