Ontology based multiobject segmentation and classification in sports videos

Abstract

The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes.