Unconcealed Gun Detection using Haar-like and HOG Features - A Comparative Approach Sorath Asnani, Syed Danial Waseem, Ali Asghar Manjotho Abstract –– Due to its wide variety of applications, object detection has been the center of attention for researchers in the field of digital image processing and computer vision. When trained with the sample training dataset, various object classifiers can detect and classify the objects with prominent accuracy and precision. The major step in any of the object classification algorithm is feature selection. Performance of the classifier depends on robustness of the feature vector selected. This paper presents unconcealed gun detection method by using Boosted Cascade Classifier. The classifier was trained with two of the widely known feature types: Haar-like features and Histogram of Oriented Gradients (HOG) features. The paper also presents a comparative study between the two of the feature types under the consideration of unconcealed gun detection. The classifier was trained with the dataset of 11,257 number of images using both the types of features separately and tested with dataset of 700 number of images. Using the Haar-like features the classifier attained the accuracy of 42.14% with the precision of 45.73%. While using the HOG features, the classifier gained the accuracy of 88.57% with the precision of 95.30%. The evaluation metrics clearly depicts the superiority of HOG features over the Haar-like features in unconcealed gun detection. Keywords –– Object Detection, Cascade Classifier, Haar-like features, HOG features I. INTRODUCTION Unconcealed weapon detection is a modern research area in the field of Computer Vision. It is an application of smart surveillance which provides a guaranteed, cost efficient and accurate security system. Weapon detection is not a new field for research because much work has been done on concealed weapon detection. The interesting point is that Unconcealed Weapon Detection is a relatively new field of research. To detect any object, a classifier is trained. A classifier is nothing but a computer program which is capable to identify an object of interest among all other objects. A classifier needs some input to process so that it can be trained accordingly. Input to the classifier is in the form of training images. An image not only contains the object of interest but it has other objects as well. For example, the image of gun may contain a person holding that gun, a person to which the gun is being pointed, the environmental background and so on. So it is not suggested to provide whole image to a classifier. Rather, it is more beneficial to extract some features from the object of interest in an image. Feature is simply a piece of information related to the object in an image. A feature may typically be the shape, edge, color, size or texture of object. The more strong the feature set, the more accurate is the classifier. So the most important step in training a classifier is to decide the feature set and to select a robust feature extraction technique. There are various feature extraction algorithms used in practice today for the purpose of classification, few of them are: SIFT (Scale Invariant Feature Transform) [1], SURF (Speeded-up Robust Features) [2], FAST (Fusing Points and Lines for High Performance Tracking) [3], MSER (Maximally Stable External Regions) [4], BRISK (Binary Robust Invariant Scalable Key-points) [5], HOG (Histogram of Oriented Gradients) [6], Haar wavelets [7], LBP (Local Binary Patterns) [8] and many others. The biggest problem in object detection is to find the most appropriate feature type for the particular application. Literature survey reveals that Haar and HOG feature types are the most powerful features from all the above mentioned features. But at the same time we cannot ignore the importance of other features in forming the basis for the development of these very famous feature types. This paper provides a comparative study between the two well-known feature types i.e. HOG and Haar-like features for the purpose of unconcealed weapon detection. The HOG features, introduced by Dalal and Triggs in [6], are well known for their high detection accuracy whereas the Haar-like features, proposed by Viola and Jones in [7] are famous for their fast training speed. This paper proposes a practical approach to validate the above properties of both the feature types in case of unconcealed gun detection. The rest of the paper is ordered as follows: Section II outlines the previous work, Section III provides the details Department of Computer Systems Engineering Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan. sorath.asnani@hotmail.com, engr.syeddanialwaseem@outlook.com, ali.manjotho@faculty.muet.edu.pk ASIAN JOURNAL OF ENGINEERING, SCIENCES & TECHNOLOGY, VOL. 4, ISSUE 1 MARCH 2014 34