Moving object tracking based on geogram

We introduce the concept of a geogram that captures richer features to represent the objects. The spatiogram contains some moments upon the coordinates of the pixels corresponding to each bin while the geogram contains information about the perimeter of grouped regions in addition to features in the spatiogram. The perimeter of the given region has capability to represent the geometrical compactness on the distribution of the given feature. The geogram-based feature descriptor increases the accuracy of tracking because it can capture the features in lower levels. Also, we consider that a convergence of the mean shift algorithm for the spatiogram is divided into obvious dynamic and steady states as well as in automatic control, and introduce a hybrid technique of the geogram and the histogram to control convergence process. Moreover, we derive a mean shift procedure for the proposed geogram. We test our feature descriptor and measure in object tracking scenario. Experimental results demonstrate that the geogram has promising discriminative capability in comparison with other ones.