Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for both, surveillance and control purposes. We reformulate this detection problem into the problem of minimizing false positives and solve it by building the key training data-set guided by the results of a deep Convolutional Neural Networks (CNN) classifier, then assessing the best classification model under two approaches, the sliding window approach and region proposal approach. The most promising results are obtained by Faster R-CNN based model trained on our new database. The best detector show a high potential even in low quality youtube videos and provides satisfactory results as automatic alarm system. Among 30 scenes, it successfully activates the alarm after five successive true positives in less than 0.2 seconds, in 27 scenes. We also define a new metric, Alarm Activation per Interval (AApI), to assess the performance of a detection model as an automatic detection system in videos.
The first and traditional sub-area in gun detection focuses on detecting concealed handguns in X-ray or millimetric wave images. The most representative application in this context is luggage control in airports. The existent methods achieve high accuracies by using different combinations of feature extractors and detectors, either using simple density descriptors @cite_28 , border detection and pattern matching @cite_1 or using more complex methods such as cascade classifiers with boosting @cite_15 . The effectiveness of these methods made them essential in some specific places. However, they have several limitations. As these systems are based on met al detection, they cannot detect non met allic guns. They are expensive to be used in many places as they require to be combined with X-ray scanners and Conveyor belts. They are not precise because they react to all met allic objects.
@cite_28 @cite_15 @cite_1
2016107543 1646190092 2113859792 1137062290
The goal of this research is to develop a process, using current imaging hardware and without human intervention, that provides an accurate and timely detection alert of a concealed weapon and its location in the image of the luggage. There are several processes in existence that are able to highlight or otherwise outline a concealed weapon in baggage but so far those processes still require a highly trained operator to observe the resulting image and draw the correct conclusions. We attempted three different approaches in this project. The first approach uses edge detection combined with pattern matching to determine the existence of a concealed pistol. Rather than use the whole body of the weapon which varies significantly, the trigger guard was used since it is fairly consistent in dimensions. While the processes were reliable in detecting a pistol's presence, on any but the simplest of images, the computational time was excessive and a substantial number of false positives were generated. The second approach employed Daubechie wavelet transforms but the results have so far been inconclusive. A third approach involving an algorithm based on the scale invariant feature transform (SIFT) is proposed. A method is proposed for automatic detection of the concealed pistols detected by passive millimeter in security applications. In this paper, we extend four half-surrounded Haar-like features and use integral image to rapidly calculate the rectangle features. Then we obtain a multi-layer classifier cascaded by several strong classifiers using AdaBoost algorithm to detect the contraband. Various passive millimeter images from both published literatures and our own measurements are used for training and testing. The experimental results show that the met allic pistols in different sizes, shapes, and angles can be accurately detected, so this method is useful for automatic detection of pistols. Thanks to the development of Mobile mapping systems (MMS), street object recognition, classification, modelling and related studies have become hot topics recently. There has been increasing interest in detecting changes between mobile laser scanning (MLS) point clouds in complex urban areas. A method based on the consistency between the occupancies of space computed from different datasets is proposed. First occupancy of scan rays (empty, occupied, unknown) are defined while considering the accuracy of measurement and registration. Then the occupancy of scan rays are fused using the Weighted Dempster‐Shafer theory (WDST). Finally, the consistency between different datasets is obtained by comparing the occupancy at points from one dataset with the fused occupancy of neighbouring rays from the other dataset. Change detection results are compared with a conventional point to triangle (PTT) distance method. Changes at point level are detected fully automatically. The proposed approach allows to detect changes at large scales in urban scenes with fine detail and more importantly, distinguish real changes from occlusions. In aviation security, checked luggage is screened by computed tomography scanning. Met al objects in the bags create artifacts that degrade image quality. Though there exist met al artifact reduction (MAR) methods mainly in medical imaging literature, they require knowledge of the materials in the scan, or are outlier rejection methods.To improve and evaluate a MAR method we previously introduced, that does not require knowledge of the materials in the scan, and gives good results on data with large quantities and different kinds of met al.We describe in detail an optimization which de-emphasizes met al projections and has a constraint for beam hardening and scatter. This method isolates and reduces artifacts in an intermediate image, which is then fed to a previously published sinogram replacement method. We evaluate the algorithm for luggage data containing multiple and large met al objects. We define measures of artifact reduction, and compare this method against others in MAR literature.Met al artifacts were reduced in our test images, even for multiple and large met al objects, without much loss of structure or resolution.Our MAR method outperforms the methods with which we compared it. Our approach does not make assumptions about image content, nor does it discard met al projections.