Gang Yu, Junsong Yuan, and Zicheng Liu
29 October 2012
Early recognition and prediction of human activities are of great importance in video surveillance, e.g., by recognizing a criminal activity at its beginning stage, it is possible to avoid unfortunate outcomes. We address early activity recogni- tion by developing a Spatial-Temporal Implicit Shape Mod- el (STISM), which characterizes the space-time structure of the sparse local features extracted from a video. The ear- ly recognition of human activities is accomplished by pat- tern matching through STISM. To enable efficient and ro- bust matching, we propose a new random forest structure, called multi-class balanced random forest, which makes a good trade-off between the balance of the trees and the dis- criminative abilities. The prediction is done simultaneously for multiple classes, which saves both the memory and com- putational cost. The experiments show that our algorithm significantly outperforms the state of the arts for the human activity prediction problem.
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