Método de detección automática de armas de mano en video usando aprendizaje profundo

Investigación TecnológicaEste experimento implementa un método basado en aprendizaje profundo para el apoyo del proceso de monitoreo y detección de armas de mano (revólveres y pistolas), de forma automática con el fin de proponer una solución a la problemática de seguridad existente en la ciudad de Bogotá. Se propone una metodología que consta de siete (7) pasos: construcción del conjunto de datos, pre procesamiento de los videos, extracción de características de los frames, muestreo del conjunto de datos, red de regiones propuestas, clasificación y detección de armas de mano, y rendimiento del modelo de detección de armas de mano en video.RESUMEN INTRODUCCIÓN 1. GENERALIDADES 2. MARCO DE REFERENCIA 3. METODOLOGÍA 4. DISEÑO METODOLÓGICO 5. DISCUSIÓN DE RESULTADOS 6. CONCLUSIONES 7. RECOMENDACIONES 8. ANEXOS 9. BIBLIOGRAFÍAPregradoIngeniero de Sistema

The detection of handguns from live-video in real-time based on deep learning

Many people have been killed indiscriminately by the use of handguns in different countries. Terroristacts, online fighting games and mentally disturbed people are considered the common reasons for these crimes.  A real-time handguns detection surveillance system is built to overcome these badacts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue,the designed detection system sends an alert message to the supervisor when aweapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgundetection operation. This model has been selected becauseit is fast and accurate in infering to integrate network for detecting and classifying weaponsin images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model bothenhance the accuracy level in identifying the real time handguns detection

An Expert System for Weapon Identification and Categorization Using Machine Learning Technique to Retrieve Appropriate Response

In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved

Deep learning small arms recognition: Development of a basic model and prospects for its use in the field of conventional disarmament

The automated detection, recognition, and identification of small arms through deep learning tools is a recent process that seems to offer interesting possibilities in the field of conventional disarmament. As the field of research has so far mainly focused on detection models in the context of domestic security, it is interesting to explore, in this paper, the development of a basic small arms recognition model and its potential use in the field of conventional disarmament; this paper lays the foundations of a basic small arms recognition model through its development using deep learning tools and its experimental testing. The initial results of the basic model developed in this paper put in perspective the foundations for improvement towards a developed recognition model and towards a complex identification model of small arms. Moreover, this paper also puts in perspective the potential of such models in the field of conventional disarmament

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

Handgun detection using combined human pose and weapon appearance

Closed-circuit television (CCTV) systems are essential nowadays to prevent security threats or dangerous situations, in which early detection is crucial. Novel deep learning-based methods have allowed to develop automatic weapon detectors with promising results. However, these approaches are mainly based on visual weapon appearance only. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible. In this work, a novel method is proposed to combine, in a single architecture, both weapon appearance and human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed in different subnetworks and combined to produce the handgun bounding box. Results obtained show that the combined model improves the handgun detection state of the art, achieving from 4.23 to 18.9 AP points more than the best previous approach.Comment: 17 pages, 18 figure

The Need for Marker-Less Computer Vision Techniques for Human Gait Analysis on Video Surveillance to Detect Concealed Firearms

Crimes involving the use of firearms have been on the increase in the past few years. One of the measures adopted to prevent these crimes is the use of CCTV operators at video surveillance centers to detect persons carrying concealed firearms on their bodies by monitoring their behavior. This paper has found that this technique has challenges associated with human weaknesses and errors. A review of the current attempts to automate video surveillance for concealed firearm detection has found that they have the limitation that the techniques can only be employed on stationary and cooperative persons. This makes them inappropriate for real-life surveillance. This paper highlights the need for automated video surveillance solutions that can detect persons carrying concealed firearms when they are not stationary and aware of the scanning process. We further explore automated behavioral analysis and specifically gait analysis as a possible technique for concealed firearm detection on video surveillance. Lastly, the paper highlights the possibility and viability of human gait analysis using marker-less computer vision techniques for detecting persons carrying firearms on their waist line