Introduction

This page contains software and instructions for Aligned Cluster Analysis (ACA) [1] and its hierarchical version (HACA) [2]. All the functions have been written and documented in Matlab format. We additionally provide C++ implementations of some dynamic programming routines which involve many loops and are notoriously slow in Matlab.

Installation

unzip aca.zip to your folder;
Run make.m to compile all C++ files;
Run addPath.m to add sub-directories into the path of Matlab.
Run demoXXX file.

Instructions

The package of aca.zip contains three folders, two setup files and three demo files.

./data: This folder contains motion capture data.
./src: This folder contains the main implmentation of ACA and HACA.
./lib: This folder contains some necessary library functions.
./make.m: Matlab makefile for C++ code.
./addPath.m: Adds the sub-directories into the path of Matlab.
./demoToy.m: Segmentation of a synthetic sequence by ACA.
./demoToyH.m: Segmentation of a synthetic sequence by HACA.
./demoMocap.m: Segmentation of motion capture sequence by ACA and HACA. By using this function, you can obtain results similar to those shown here.

Other Tips

For each C++ code, we provide its corresponding Matlab version. For instance, you can use acaFordSlow.m instead of acaFord.cpp. They have the same interface in both input and output. The C++ code is faster to obtain result while the Matlab version is easier to understand and debug.

For the DTAK algorithm and ACA algorithm, we also provide a version in which each searching step in dynamic programming can be locally constrained. Although we didn't use this feature in our paper, we found it is useful to obtain a robust alignment and to speedup the algorithm. Please refer to [3] [4] for more details on constraints in Dynamic Time Warping (DTW).

References

[1] F. Zhou, F. De la Torre, and J. K. Hodgins, "Aligned cluster analysis for temporal segmentation of human motion," in International Conference on Automatic Face and Gesture Recognition (FG), 2008.

[2] F. Zhou, F. De la Torre, and J. K. Hodgins, "Hierarchical Aligned cluster analysis for temporal segmentation of human motion," submitted to IEEE Transactions on Pattern Analysis & Machine Intelligence (PAMI), 2010.

[3] S. Salvador and P. Chan, "Toward accurate Dynamic Time Warping in linear time and space," in Intelligent Data Analysis, 2007, pp. 561-580.

[4] L. Rabiner and B.-H. Juang, "Fundamentals of speech recognition," Prentice Hall, 1993.

Copyright

This software is free for use in research projects. If you publish results obtained using this software, please use this citation.

@inproceedings{Zhou_2008_6155,
author    = {Feng Zhou and Fernando De la Torre and Jessica K. Hodgins},
title     = {Aligned Cluster Analysis for Temporal Segmentation of Human Motion},
booktitle = {IEEE Conference on Automatic Face and Gestures Recognition (FG)},
month     = {September},
year      = {2008},
}

If you have any question, please feel free to contact Feng Zhou (zhfe99@gmail.com).