Gun detection in surveillance videos using deep neural networks

Junyi Lim, Md Istiaque Al Jobayer, Vishnu Monn Baskaran, Joanne Munyee Lim, Koksheik Wong, John See

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

21 Citations (Scopus)


The ongoing epidemic of gun violence worldwide has compelled various agencies, businesses and consumers to deploy closed-circuit television (CCTV) surveillance cameras in attempt to combat this epidemic. An active-based CCTV system extends this platform to autonomously detect potential firearms within a video surveillance perspective. However, autonomously detecting a firearm across varying CCTV camera angles, depth and illumination represents an arduous task which has seen limited success using existing deep neural networks models. This challenge is in part due to the lack of available contextual hand gun information from CCTV images, which remains unresolved. As such, this paper introduces a novel large scale dataset of hand guns which were captured using a CCTV camera. This dataset serves to substantially improve the state-of-the-art in representation learning of hand guns within a surveillance perspective. The proposed dataset consist of 250 recorded CCTV videos with a total of 5500 images. Each annotated CCTV image realistically captures the presence of a hand gun under 1) varying outdoor and indoor conditions, and 2) different resolutions representing variable scales and depth of a gun relative to a cameras sensor. The proposed dataset is used to train a single-stage object detector using a multi-level feature pyramid network (i.e. M2Det). The trained network is then validated using images from the UCF crime video dataset which contains real-world gun violence. Experimental results indicate that the proposed dataset increases the average precision of gun detection at different scales by as much as 18% when compared to existing approaches in firearms detection.

Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2019)
EditorsTatsuya Kawahara, Jiangyan Yi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781728132488
ISBN (Print)9781728132495
Publication statusPublished - 2019
EventAnnual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA) 2019 - Lanzhou, China
Duration: 18 Nov 201921 Nov 2019 (Proceedings) (Website)

Publication series

Name2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PublisherInstitute of Electrical and Electronics Engineers, Inc.
ISSN (Print)2640-009X
ISSN (Electronic)2640-0103


ConferenceAnnual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA) 2019
Abbreviated titleAPSIPA ASC 2019
Internet address


  • Active video surveillance
  • Deep neural networks
  • Gun detection

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