Real-time event classification in field sport videos

The paper presents a novel approach to real-time event detection in sports broadcasts. We present how the same underlying audio-visual feature extraction algorithm based on new global image descriptors is robust across a range of different sports alleviating the need to tailor it to a particular sport. In addition, we propose and evaluate three different classifiers in order to detect events using these features: a feed-forward neural network, an Elman neural network and a decision tree. Each are investigated and evaluated in terms of their usefulness for real-time event classification. We also propose a ground truth dataset together with an annotation technique for performance evaluation of each classifier useful to others interested in this problem

Restricted Boltzmann machine as an aggregation technique for binary descriptors

The article presents a novel approach to the challenge of real-time image classification with deep neural networks. The proposed architecture of the neural network exploits computationally efficient local binary descriptors and uses a Restricted Boltzmann Machine (RBM) as a feature space projection step so that the resulting depth of the deep neural network can be reduced. A Contrastive Divergence procedure is used both for RBM training and for feature projection. The resulting neural networks exhibit performance close to the current state of the art but are characterized by a small model memory footprint (i.e., number of parameters) and extremely efficient computational complexity (i.e, response time). The low number of parameters makes these architectures applicable in embedded systems with limited memory or reduced computational capabilities