Abstract
In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the low-texture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.
Highlights
At present, the methods of pattern recognition, target classification, and detection based on local feature have been widely used in many aspects, such as in medicine,[1] in the industry,[2] in biology,[3] and so on
The most common template matching method based on the pixel value is through sliding the template on another image to find the most similar area,[10,11] and fast template matching is achieved through normalized cross-correlation (NCC)
We make an experiment to compare our method with the NCC template matching method[10,11] and the template matching method based on histograms of gradients (HOG) features.[20]
Summary
The methods of pattern recognition, target classification, and detection based on local feature have been widely used in many aspects, such as in medicine,[1] in the industry,[2] in biology,[3] and so on. To solve these problems, we use the template matching method, because it has less computational complexity and it does not need to spend much time on training.[8] This method of area matching is suitable for the situations where the background is complex and the target texture is less.[9] We maintain the traditional idea that builds a simple training set first, because this method is robust when the targets are rotated or deformed.
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