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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