Knife Detection Research Articles

An Optimized Mask R-CNN with Bag-of-Visual Words and Fast+Surf Algorithm in Sharp Object Instance Segmentation for X-ray Security

Automated security X-ray analysis is highly desired for efficiently inspecting sharp objects. The research formulated an optimized approach for sharp object detection using a Mask R-CNN architecture. The dataset used during the training phase consists of 238 balanced raw images extracted from GitHub named OPIXray. The researchers utilized recent advances in computer vision algorithms, including the Bag-of-Words and Fast+Surf feature extraction techniques, to improve the accuracy and reliability of object deletion. The research demonstrated that the optimized versions of the classification and object detection models have significantly improved accuracy for most categories, with a 5% improvement for the clear category and a 3% improvement for both the scissor and straight knife detection.

A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras

The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone.

Adaptive Technique for Brightness Enhancement of Automated Knife Detection in Surveillance Video with Deep Learning

Detecting knives in surveillance videos are very urgent for public safety. In general, the research in identifying dangerous weapons is relatively new. Knife detection is a very challenging task because knives vary in size and shape. Besides, it easily reflects lights that reduce the visibility of knives in a video sequence. The reflection of light on the surface of the knife and the brightness on its surface makes the detection process extremely difficult, even impossible. This paper presents an adaptive technique for brightness enhancement of knife detection in surveillance systems. This technique overcomes the brightness problem that faces the steel weapons and improves the knife detection process. It suggests an automatic threshold to assess the level of frame brightness. Depending on this threshold, the proposed technique determines if the frame needs to enhance its brightness or not. Experimental results verify the efficiency of the proposed technique in detecting knives using the deep transfer learning approach. Moreover, the most four famous models of deep convolutional neural networks are tested to select the best in detecting knives. Finally, a comparison is made with the-state-of-the-art techniques, and the proposed technique proved its superiority.

Automated Detection of Firearms and Knives in a CCTV Image.

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

CCTV object detection with fuzzy classification and image enhancement

In this paper we propose a novel approach for pattern recognition problems with non-uniform classes of images. The main concept of this classification method is to describe classes of images with their fuzzy portraits. This approach is a good generalization of the algorithm. The fuzzy set is calculated as a preliminary result of the algorithm before the final decision or rejection that solves the problem of uncertainty at the boundaries of classes. We use the method to solve the problem of knife detection in still images. The main aim of this paper is to test fuzzy classification with feature vectors in a real environment. We used selected MPEG-7 descriptor schemes as feature vectors. The method was experimentally validated on a dataset of over 12,000 images. The article describes the results of six experiments which confirm the accuracy of our method. In addition we conducted a test with enhanced images, achieved with two state-of-the-art super-resolution algorithms.

Visual detection of knives in security applications using Active Appearance Models

In this paper, a novel application of Active Appearance Models to detecting knives in images is presented. In contrast to its popular applications in face segmentation and medical image analysis, we not only use this computer vision algorithm to locate an object that is known to exist in an analysed image, but---using an interest point typical of knives---also try to identify whether or not a knife exists in the image in question. We propose an entire detection scheme and examine its performance on a sample test set. The work presented in this paper aims to create a robust visual knife-detector to be used in security applications.

An approach to robust visual knife detection

Computerised monitoring of CCTV images is attracting a lot of attention both from potential end-users seeking to increase the effectiveness of their video surveillance systems and as a popular rese...