Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

Ingle, Palash Yuvraj; Kim, Young-Gab

doi:10.3390/s22103862

Cited by 96Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsHuman Pose and Action Recognition

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

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Ingle, Palash Yuvraj, Kim, Young-Gab·2022·Sensors

DOI 10.3390/s22103862PMC PMC9143895

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Abstract

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera.

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MSD-CNN achieves 90.7% accuracy with respect to parameters such as different types of guns and knives, real-time deployment, and multi-view cameras.
“The MSD-CNN achieves 90.7% accuracy with respect to parameters such as different types of guns and knives, real-time deployment, and multi-view cameras.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [129]
Section 4.2 reports that MSD-CNN achieved 90.7% precision for detecting different types of guns and knives with real-time deployment and multiview cameras. The citing sentence calls that figure accuracy, but the passage identifies it as precision. (LLM, conf=0.90)
Experimental — accuracy not yet measured
MSD-CNN is designed to be lightweight to allow creation of multiple instances of the model.
“This MSD-CNN model is designed to be lightweight, to allow for the creation of multiple instances of the model.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [129]
By applying MSD-CNN to each video sequence individually using dynamic programming, one can simultaneously detect abnormal objects in multi-view cameras without significantly increasing computational overhead.
“By applying the model to each video sequence individually using dynamic programming, one can simultaneously detect abnormal objects in multi-view cameras without significantly increasing the computational overhead.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [129]
MSD-CNN is a lightweight multi-class subclass detection CNN used to classify incoming video data into abnormal frames and normal frames in 2022.
“In [129], a lightweight multi-class subclass detection CNN (MSD-CNN) was used to classify incoming video data into abnormal frames (dangerous events such as carrying guns or knives) and normal frames (events such as walking or office work) in 2022.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [129]

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Authors: Ingle, P. Y., & Kim, Y.-G.
Affiliations: Sejong University (KR)
Corresponding author: Kim, Young-Gab
ORCID: Ingle, Palash Yuvraj
Kim, Young-Gab
Venue: Sensors
Publisher: MDPI AG
Published: 2022-05-19
Volume: 22
Issue: 10
Pages: 3862
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s22103862
PMC: PMC9143895
OpenAlex: W4280603059
Cited by: 96
References: 35
Citations by year: 2026: 11 · 2025: 30 · 2024: 26 · 2023: 24 · 2022: 5
FWCI: 6.95
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Institute of Information and Communications Technology Planning and Evaluation (IITP) (2019-0-00231)
Korea government (MSIT) (2019-0-00231)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Keywords: video surveillance, smart cities, lightweight convolutional neural network, real-time detection, weapon detection, handgun detection, knife detection, resource-constrained devices, object localization
MeSH terms: Cities, Humans, Pattern Recognition, Automated, Neural Networks, Computer

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Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

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