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.