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Terror attacks are increased worldwide. The early detection of weapons is an important objective for security specialists. In this paper, we proposed an automated surveillance system for detecting fire weapons in cluttered scene. First SIFT features are extracted from the collection of images. Second, K-means clustering is adopted for clustering the SIFT features. Third, a word vocabulary based histogram is implemented by counting occurrences of the extracted clusters in each image. The histogram is the input to Support Vector Machine that will be trained on the collection of images. Finally, the trained SVM is the system classifier that will decide if new image contains a weapon or not. The main contributions of the paper is to adopt the visual words classification scheme in detecting fire weapons. In addition, we used RANSAC to reduce the matching outliers. The system showed high accuracy in detecting fire weapons in images and video surveillance systems.

Bag of Words Based Surveillance System Using Support Vector Machines