Object detection in security applications using dominant edge directions

• We introduce “dominant edge directions” (DED) – new image features.
• DED are based on image edge distribution.
• We present an application of computer-aided video surveillance – knife detection.
• We use a combined DED and histograms of oriented gradients (HOG) detector.
• The combined use of DED and HOG speeds up detection roughly fivefold over HOG alone.

This paper presents a novel approach to object detection in images. We build on the existing work on detecting knives in images, which has previously attempted to solve the problem by using the well-established histogram of oriented gradients (HOG) features. We introduce a new feature set that allows for rapid initial object location in images, and can then be followed by the use of an object specific detector. This approach allows for speeding up the overall detection process, which has been demonstrated on the example of knives, and is in the position of bringing many object detectors closer to real-time execution speeds.