Efficient Human Action and Gait Analysis Using Multiresolution Motion Energy Histogram

Chih-Chang Yu,Kuo-Chin Fan,Chien-Hung Cheng,Hsu-Yung Cheng

doi:10.1155/2010/975291

Abstract

Average Motion Energy (AME) image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH). To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

Highlights

Analyzing human’s behavior or identity is a very interesting research topic because human is usually the most concerned object in many applications such as surveillance system or video understanding
We propose an uneven partitioning method to address the important part of Motion Energy Histogram (MEH) automatically and apply an efficient histogram matching algorithm by utilizing the characteristic of multiresolution histogram
We propose a method to construct the multiresolution motion energy histogram (MRMEH) by using the results of quadtree decomposition on MEH

Summary

Introduction

Analyzing human’s behavior or identity is a very interesting research topic because human is usually the most concerned object in many applications such as surveillance system or video understanding This problem is usually solved by two kinds of approaches: video-based approaches or sensor-based approaches [1, 2]. The authors obtained the MEI by collecting a group of frames and extract scale invariant features for recognition This idea was extended to the so called average motion energy (AME) by aligning and normalizing the foreground silhouettes [15]. The computation of SAD is inefficient when the size of image is large because the computational time is relevant to the image size This problem is not severe when the amount of database images is small but can be expected with the increasing size of image database.

Histogram-Based Method

Experiments

A2 A3 A4 A5 A6 A7

Findings

Conclusion