Detection Of Concealed Weapons Using Image Processing Techniques: A Review

Mahajan, Rohini; Padha, Devanand

doi:10.1109/icsccc.2018.8703346

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Not only the techniques of recognizing the features of a moving object are explained in this research work but also its explained that how to classify the concealed objects present in the video frames. This paper also reviews some research gaps like it is difficult to identify the concealed objects in loose cloths and the shape and size of weapon varies, etc [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Arif¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

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The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient-Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism. This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension. The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution. Efficient-Net algorithm gives better results than existing algorithms. By using Efficient-Net algorithms the accuracy achieved 98.12% when epochs increase as compared to other algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Arif¹,

Shahzad²,

Iqbal³

et al. 2022

Computers, Materials &Amp; Continua

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“…Several works addressed reducing the number of FP and FN in handgun detection in realistic video surveillance environments using pre-processing [16,4] and post-processing approaches [26,19]. For example, the authors in [4] presented a pre-processing technique that help improving the quality of the videos in changing illumination environments.…”

Section: Related Workmentioning

confidence: 99%

“…The first studies in this context improved the detection in videos by building new datasets [17,10,27,12,11]. The most recent studies applied preor post-processing techniques to further reducing the errors [16,26,4,19]. While other studies used data fusion to improve the overall performance [25,2,18,1,23].…”

Section: Introductionmentioning

confidence: 99%

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

Olmos,

Tabik,

Perez-Hernandez

et al. 2021

Preprint

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Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable. Using such detectors still implies a high number of false alarms and false negatives. In this context, most existent studies select one of the latest single-image detectors and train it on a better dataset or use some preprocessing, post-processing or data-fusion approach to further reduce false alarms. However, none of these works tried to exploit the temporal information present in the videos to mitigate false detections. This paper presents a new system, called MULTI Confirmation-level Alarm SysTem based on Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM) (MULTI-CAST), that leverages not only the spacial information but also the temporal information existent in the videos for a more reliable handgun detection. MULTICAST consists of three stages, i) a handgun detection stage, ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal confirmation stage. The temporal confirmation stage uses the positions of the detected handgun in previous instants to predict its trajectory in the next frame. Our experiments show that MULTI-CAST reduces by 80% the number of false alarms with respect to Faster R-CNN based-single-image detector, which makes it more useful in providing more effective and rapid security responses.

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“…Target localization has various civilian and defence applications [1]- [3]. Alternative applications of a typical radar frontend are communication channel estimation or imaging applications including detection of concealed weapons [4,5]. Electronically steered phased-array radars [6,7] are widely used due to their narrow, scanning main lobe which can be accurately positioned over a wide field of view for resolving individual targets.…”

Section: Introductionmentioning

confidence: 99%

Concept analysis of a frequency-sweeping delta/sigma beam-switching radar using machine learning

Goldar

Hassanpour

Oberhammer

2022

2021 18th European Radar Conference (EuRAD)

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This paper investigates a novel radar concept that is based on a minimalistic, small-aperture antenna array but features intelligent beam-shape switching and artificialintelligence signal processing. In contrast to conventional phasedarrays, size, cost, and hardware complexity are drastically reduced by the proposed dual-antenna array which can create a broad and a frequency-scanning notched beam shape. The angular-resolution and target discrimination performance of the proposed radar concept have been validated by radar simulations for single and multiple target scenarios. For the signal processing, two convolutional neural networks (CNN) and a multilayer perceptron model are benchmarked against each other. A further CNN is implemented for estimating the number of targets, which can be used to pre-select the type of network determining range and cross-range of multiple targets. This paper shows that a small antenna aperture frontend in combination with beam-shape switching and artificial-intelligence signal processing methods is a suitable hardware-efficient radar concept for accurate multitarget location.

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Detection Of Concealed Weapons Using Image Processing Techniques: A Review

Cited by 10 publications

References 10 publications

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

Concept analysis of a frequency-sweeping delta/sigma beam-switching radar using machine learning

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