Rotationally Invariant Object Detection on Video Using Zernike Moments Backed with Integral Images and Frame Skipping Technique

This is a follow-up study on Zernike moments applicable in detection tasks owing to a construction of complex-valued integral images that we have proposed in [3]. The main goal of the proposition was to calculate the mentioned features fast (in constant-time). The proposed solution can be applied with success when dealing with single images, however it is still too slow to be used in real-time applications, for example in video processing. In this work we attempted to solve mentioned problem. In this paper we propose a technique in order to reduce the detection time in real-time applications. The degree of reduction is controlled by two parameters: fs (related to the gap between frames that undergo a full scan) and nb (related to the size of neighborhood to be searched on non-fully scanned frames). We present a series of experiments to show how our solution performs in terms of both detection time and accuracy.