Energy transfer features combined with DCT for object detection

Abstract

The basic idea behind the energy transfer features is that the appearance of objects can be described using a function of energy distribution in images. Inside the image, the energy sources are placed and the energy is transferred from the sources during a certain chosen time. The values of energy distribution function have to be reduced into a reasonable number of values. The process of reducing can be simply solved by sampling. The input image is divided into regular cells. The mean value is calculated inside each cell. The values of samples are then considered as a vector that is used as an input for the SVM classifier. We propose an improvement to this process. The discrete cosine transform coefficients are calculated inside the cells (instead of the mean values) to construct the feature vector for the face and pedestrian detectors. To reduce the number of coefficients, we use the patterns in which the coefficients are grouped into regions. In the face detector, the principal component analysis is also used to create the feature vector with a relatively small dimension. The results show that, using this approach, the objects can be efficiently encoded with a relatively short vector with the results that outperform the results of the state-of-the-art detectors.