Asian Journal of Engineering, Sciences and Technology - Volume 4, Issue 1 2014
By Sorath Asnani, Syed Danial Waseem, Ali Asghar Manjotho
Keywords: Object Detection, Cascade Classifier, Haar-like features, HOG features
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.