Fast multi-class action recognition by querying inverted index tables

Abstract

A fast inverted index based algorithm is proposed for multi-class action recognition. This approach represents an action as a sequence of action states. Here, the action states are cluster centers of the extracted shape-motion features. At first, we compute the shape-motion features of a tracked actor. Secondly, a state binary tree is built by hierarchically clustering the extracted features. Then the training videos are represented as sequences of action states by searching the state binary tree. Based on the labeled state sequences, we create a state inverted index table and a state transition inverted index table. During testing, after representing a new action video as a state sequence, the state and state transition scores are computed by querying the inverted index tables. With the weight trained by the validation set, we get an action class score vector. The recognized action class label is the index of the maximum component of the score vector. Our key contribution is that we propose a fast multi-class action recognition approach based on two inverted index tables. Experiments on several challenging data sets confirm the performance of this approach.