Shot Trace Analysis Using The Computer Vision

Palchikova, I. G.; Latyshov, I. V.; Smirnov, E. S.; Kondakov, Alexander V.; Vasiliev, V. A.

doi:10.1109/itnt49337.2020.9253326

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use and development of computer vision methods in the tasks of expert examinations of GSR is also relevant [ 22 ]. The tasks include identifying and fixing GSR on objects, analyzing the characteristics of the main and additional gunshot traces, including measuring GSR, calculating morphology parameters [ 23 ], and the topography of their deposition. The results obtained are used in determining the direction and distance of the shot.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision in Analyzing the Propagation of a Gas–Gunpowder Jet

Palchikova

Latyshov

Smirnov

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

A method of mathematically processing the digital images of targets is developed. The theoretical and mathematical justification and the experimental validation of the possibility of estimating the amount of gunshot residue (GSR) and determining the GSR distribution over the target on the basis of its digital image is provided. The analysis of the optical density in selected concentric rings in the images reveals the radial dependence of soot distribution in the cross section of a gas–gunpowder jet. The analysis of the optical density in selected sectors of the circle reveals the angular dependence of the soot distribution in the gas–gunpowder jet cross section. It is shown that the integral optical density averaged over a selected area in the target image characterizes the mass of GSP deposited on it. It is possible to quantify the differences in the radial and angular distributions of the thickness of the GSR layer on various targets obtained both with the help of weapons of different types at the same distances and with the help of weapons of the same type at different distances, by calculating the distribution of optical density on their digital images.

show abstract

Section: Introductionmentioning

confidence: 99%