Indexing, Object Segmentation, and Event Detection in News and Sports Videos - Multimedia Database Retrieval: Technology and Applications - page 165

Chapter 7
Indexing, Object Segmentation, and Event
Detection in News and Sports Videos

Abstract A video parsing algorithm in the compressed domain is first introduced
in this chapter. The algorithm is based on the conventional solution, where
energy histograms of DC coefficients are used to calculate the distance between
consecutive I/P frames, and the DC coefficients of the P-frames are obtained by
frame conversion. The detection results are enhanced by using the ratio between two
sliding windows to amplify the transitional regions. Secondly, in order to index news
video at various levels, a template-frequency model is utilized to characterize the
spatio-temporal information of news stories. The system employing this indexing
structure is highly applicable for news-on-demand applications. Thirdly, a method
for video object segmentation using Graph Cut and histogram of oriented gradients
is presented. This method enhances the segmentation of objects that do not segment
well, due to either poor luminance distribution, weak edges, or backgrounds
with similar color and movement. Fourthly, the chapter presents an automatic
and robust method to detect human faces from video sequences that combines
feature extraction and face detection based on local normalization, Gabor wavelet
transform, and AdaBoost algorithm. Finally, an application system is presented
for the classification of American Football videos according to events of interest.
The system consists of two stages. The first stage is responsible for play event
localization and the latter stage is responsible for feature mapping and classification.
The first stage employs MPEG-7 motion activity descriptors to detect the starting
point of a play event, whereas the second stage uses MPEG-7 motion and audio
descriptors along with Mel Frequency Cepstrum Coefficient features to classify the
events using Fisher's LDA.

7.1 Introduction

News and sports video database applications require video-on-demand technology
to allow users to select and watch video content on demand. Towards the goal
of a database system which can implement on-demand technology, the system
requires tools for automatically tagging video content to support end-user interac-
tions such as search, filtering, mining, content-based routing, personalization, and
summarization. Such tools will enable the system to decompose video images into
semantic primitives that the user can employ to define “interesting” or “significant”