Human action recognition using labeled Latent Dirichlet Allocation model

Recognition of human actions has already been an active area in the computer vision domain and techniques related to action recognition have been applied in plenty of fields such as smart surveillance, motion analysis and virtual reality. In this paper, we propose a new action recognition method which represents human actions as a bag of spatio-temporal words extracted from input video sequences and uses L-LDA (labeled Latent Dirichlet Allocation) model as a classifier. L-LDA is a supervised model extended from LDA which is unsupervised. The L-LDA adds a label layer on the basis of LDA to label the category of the train video sequences, so L-LDA can assign the latent topic variable in the model to the specific action categorization automatically. What's more, due to above characteristic of L-LDA, it can help to estimate the model parameters more reasonably, accurately and fast. We test our method on the KTH and Weizmann human action dataset and the experimental results show that L-LDA is better than its unsupervised counterpart LDA as well as SVMs (support vector machines).

I. Introduction

Action recognition is to represent and track human actions using computer techniques, and then infer and category actions combined with other information such as background and surrounding environment [1]. The key techniques in the field of action recognition include extracting representative visual features from video sequences, choosing appropriate feature descriptor and designing classification model with a good performance [2]. According to the above analysis, action recognition can be divided into two level tasks: (1) feature extraction and representation at the bottom; (2) model learning and action categorization at the top. We can see the flowchart of general action recognition approach in Fig.1..