Abstract book (pdf) - ICPR 2010

In this paper, we present a calibration-free head gesture recognition system using a motion-sensor-based approach. For
data acquisition we conducted a comprehensive study with 10 subjects. We analyzed the resulting head movement data
with regard to separability and transferability to new subjects. Ordered means models (OMMs) were used for classification,
since they provide an easy-to-use, fast, and stable approach to machine learning of time series. In result, we achieved classification
rates of 85-95% for nodding, head shaking and tilting head gestures and good transferability. Finally, we show
first promising attempts towards online recognition.
09:00-11:10, Paper ThAT9.43
TrajAlign: A Method for Precise Matching of 3-D Trajectories
Aung, Zeyar, Inst. for Infocomm Res. Singapore
Sim, Kelvin, Inst. for Infocomm Res. Singapore
Ng, Wee Siong, Inst. for Infocomm Res. Singapore
Matching two 3-D trajectories is an important task in a number of applications. The trajectory matching problem can be
solved by aligning the two trajectories and taking the alignment score as their similarity measurement. In this paper, we
propose a new method called “TrajAlign” (Trajectory Alignment). It aligns two trajectories by means of aligning their
representative distance matrices. Experimental results show that our method is significantly more precise than the existing
state-of-the-art methods. While the existing methods can provide correct answers in only up to 67% of the test cases, TrajAlign
can offer correct results in 79% (i.e. 12% more) of the test cases, TrajAlign is also computationally inexpensive,
and can be used practically for applications that demand efficiency.
09:00-11:10, Paper ThAT9.44
Real-Time 3D Model based Gesture Recognition for Multimedia Control
Lin, Shih-Yao, National Taiwan Univ.
Lai, Yun-Chien, National Taiwan Univ.
Chan, Li-Wei, National Taiwan Univ.
Hung, Yi-Ping, National Taiwan Univ.
This paper presents a new 3D model-based gesture tracking system for controlling multimedia player in an intuitive way.
The motivation of this paper is to make home appliance aware of user’s intention. This 3D model-based gesture tracking
system adopts a Bayesian framework to track the user’s 3D hand position and to recognize meaning of these postures for
controlling 3D player interactively. To avoid the high dimensionality of the whole 3D upper body model, which may complicate
the gesture tracking problem, our system applies a novel hierarchical tracking algorithm to improve the system
performance. Moreover, this system applies multiple cues for improving the accuracy of tracking results. Based on the
above idea, we have implemented a 3D hand gesture interface for controlling multimedia players. Experimental results
have shown that the proposed system robustly tracks the 3D position of the hand and has high potential for controlling the
multimedia player.
09:00-11:10, Paper ThAT9.45
Motif Discovery and Feature Selection for CRF-Based Activity Recognition
Zhao, Liyue, Univ. of Central Florida
Wang, Xi, Univ. of Central Florida
Sukthankar, Gita, Univ. of Central Florida
Sukthankar, Rahul, Intel Labs Pittsburgh and Carnegie Mellon University
Due to their ability to model sequential data without making unnecessary independence assumptions, conditional random
fields (CRFs) have become an increasingly popular discriminative model for human activity recognition. However, how
to represent signal sensor data to achieve the best classification performance within a CRF model is not obvious. This
paper presents a framework for extracting motif features for CRF-based classification of IMU (inertial measurement unit)
data. To do this, we convert the signal data into a set of motifs, approximately repeated symbolic sub sequences, for each
dimension of IMU data. These motifs leverage structure in the data and serve as the basis to generate a large candidate set
of features from the multi-dimensional raw data. By measuring reductions in the conditional log-likelihood error of the
training samples, we can select features and train a CRF classifier to recognize human activities. An evaluation of our
classifier on the CMU Multi-Modal Activity Database reveals that it outperforms the CRF-classifier trained on the raw
features as well as other standard classifiers used in prior work.
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