Real time object detection using a novel adaptive color thresholding method

Object detection is an important area of research in computer vision. One of the challenges in this domain is to detect objects in real time using the minimum resources possible. In this paper, we describe a robust method for real time object detection that can be used on low-profile hardware and needs little training. This approach is based on a discrete adaptive color thresholding method. By applying a redistribution algorithm based on color specifications on the training data, the system would be able to detect colors that may appear with small changes in lighting conditions in the scene. The detection algorithm uses a spatial voting method to improve the accuracy of the result. These characteristics make this method a robust tool in ubiquitous computing and also help intelligent environments to act/react more properly by increasing their awareness of the environment.

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