Exposure fusion for concealed weapon detection

Upadhyay, Ekta; Rana, N. K.

doi:10.1109/icdcsyst.2014.6926141

Cited by 19 publications

(3 citation statements)

References 17 publications

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“…The transform being orthogonal and non-sinusoidal in nature effectively allows the decomposition of a signal into sets of rectangular and orthogonal signals called Walsh functions. These Walsh functions are made up of only two values, +1 and -1 [17]. The transformation is not weighted and provides only real values.…”

Section: Hadamard Transformmentioning

confidence: 99%

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Altaher

Abbas

2020

TELKOMNIKA

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Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect's body despite concealment.

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Section: Hadamard Transformmentioning

confidence: 99%

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Altaher

Abbas

2020

TELKOMNIKA

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“…Weapon detection is mainly categorized into traditional & [4,5,6,7,8], image processing techniques [9,10,11,12,13], Machine Learning (ML) algorithms [14,15,16] and deep learning algorithms [17,18,19,20,21,22,23]. Traditional weapon detection techniques mainly focus on thermal/infrared and X-Ray techniques for detection are expensive and excess usage leads to radiation which cause deadly diseases like cancer and malignant tumours [24].…”

Section: Introductionmentioning

confidence: 99%

Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks

Kambhatla¹,

Khaled²

2023

IJAIA

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Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.

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“…It is a common method of image registration. In the document, the overlap points of two correction images are extracted by matching the SFIT features with scale and direction invariance [11,12]. The feature operators usually used to extract overlapping corner points include Harris [13,14], Canny [15,16], and Moravec [17,18].…”

Section: Introductionmentioning

confidence: 99%

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

Zhao

Gao

Zhang

et al. 2018

Journal of Robotics

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In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

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Exposure fusion for concealed weapon detection

Cited by 19 publications

References 17 publications

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Real Time Deep Learning Weapon Detection Techniques for Mitigating Lone Wolf Attacks

Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

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