Visual detection of knives in security applications using Active Appearance Models

Andrzej Glowacz, Marcin Kmieć, Andrzej Dziech
2013 Multimedia tools and applications  
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
more » ... a sample test set. The work presented in this paper aims to create a robust visual knife-detector to be used in security applications. Keywords Active Appearance Models . Knife-detection . Computerised video surveillance . Harris interest-point detector Introduction One application of object detection in images is computer-aided video surveillance. It can be used both in security applications [15] and as legal evidence [14] . A CCTV operator usually monitors multiple video feeds at the same time; this is a complex and challenging task in terms of allocating attention effectively. One study suggests that detection rates for operators monitoring four, nine and 16 screens oscillate around 83 %, 84 % and 64 % respectively, and will drop significantly after an hour [23] . Therefore, the need to automate the process is obvious. There have been attempts at detecting suspicious events in video material [11] and recognising human activity in videos [13] . This study focuses on automatic detection of knives in images. Carrying knives in public is either forbidden or restricted in many countries; due to the fact that knives are both widely available and can be used as weapons, their detection is of high importance for security Multimed Tools Appl (2015) 74:4253-4267 using Active Appearance Models personnel. For instance, the idea of software-based knife-detection has a practical application in surveillance of the public using CCTV. Should a knife be detected, an alarm is raised and the human operator can immediately focus their attention on that very scene, and either confirm or reject that detection. Although people will almost always outperform software algorithms for object detection in images [21] , in the long run the computer could be of significant assistance to the human CCTV operator when it comes to dealing with tens of simultaneous video feeds for many hours a day. Another application of automatic knifedetection is computer-aided analysis of luggage x-ray scans. Visual detection in security applications approach is a new research area. Visual detectors designed to work in video surveillance or x-ray scanners are not widely available. Knives are a very wide class of objects of immense diversity. Moreover, they easily reflect light, which reduces their visibility in video sequences; automatic knife-detection in images therefore represents a challenging task. In this paper, a novel application of the wellestablished Active Appearance Models (AAMs) is presented. So far, these have been extensively used for medical image interpretation [1] [25] [12] , and for the matching and tracking of faces [10] [7] . Among many existing shape-modelling algorithms, such as the Active Contour Models (Snakes), we have focused on AAMs because they model not only the shape but also the appearance (that is, pixel intensities within the image region bounded by the shape). As the knife-blade typically possesses quite a uniform texture, modelling its appearance should contribute to the general resistance of the model so that it does not converge to objects that have a shape similar to that of the knife-blade. The novelty of this work is twofold. Not only has there been (to the best knowledge of the authors) no other research on knife-detection, but also AAMs have so far not been used to detect objects belonging to a general class. They have been used in what is referred to as 'detection'-as in [12]-but that is not what is meant by 'detection' in computer vision, in the strict sense of the word. By detecting, for example, a face in an image, we mean answering the question of whether there is or there is not a face in the given image [22] . This process can be characterised by two parameters: the positive and the negative detection rates. As in the case of [12] , before what is referred to as 'detection' is performed, an assumption exists that the object is somewhere in the image, and the task is to precisely locate it. For instance, given an image of a face, finding the nose is not a task of detection since we can assume that all faces have noses. It is, rather, the task of location, and can be characterised by the level of localisation accuracy, but not by positive and negative detection rates. In this case, the assumption is that there always is a face in the analysed image. Should the AAM be performed on a non-face image, it would converge to the parts of the image whose appearance is closest to its model. This is still theoretically correct, but makes no sense from a practical point of view. Moreover, AAMs are sensitive to the initial location of their landmark points in the analysed image. Even if there is a face somewhere in a large image, for the algorithm to correctly segment it into elements, the initial location of its landmark points needs to be roughly around the face region. A common technique for face segmentation with AAMs is the use of Viola and Jones's face detector [16] to initialise the AAM as in [6] . In this paper, a method for detection of objects, in this case knives, using AAMs is introduced. It aims to answer the question of whether or not there a knife exists in the given image. Active Appearance Models AAMs were introduced [4] in 1998 as a generalisation of the popular Active Contour Model (Snake) and Active Shape Model algorithms. They are a learning-based method, which was
doi:10.1007/s11042-013-1537-2 fatcat:iehwcd2dfvdofgwz3nam3mrm2i