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Learning Spatio-temporal Features for Efficient Event Detection

This thesis has addressed the topic of event detection in videos, which is a challenging problem as events to be detected, can be complex, correlated, and may require the detection of different objects and human actions. To address these challenges, the thesis has developed effective strategies for learning the spatio-temporal features of events. Improved event detection performance has been demonstrated on several real-world challenging databases. The outcome of our research will be useful for a number of applications including human computer interaction, robotics and video surveillance.