Abstract book (pdf) - ICPR 2010

14:50-15:10, Paper WeBT7.5
Gaussian ERP Kernel Classifier for Pulse Waveforms Classification
Zuo, Wangmeng, Harbin Inst. of Tech.
Zhang, Dongyu, Harbin Inst. of Tech.
Zhang, David, The Hong Kong Pol. Univ.
Wang, Kuanquan, Harbin Inst. of Tech.
Li, Naimin, Harbin Inst. of Tech.
While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional
Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem.
To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN)
classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric
property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier.
The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental
results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.
WeCT1 Marmara Hall
Tracking and Surveillance - IV Regular Session
Session chair: Carneiro, Gustavo (Technical Univ. of Lisbon)
15:40-16:00, Paper WeCT1.1
Human 3D Motion Recognition based on Spatial-Temporal Context of Joints
Zhao, Qiong, Univ. of Science and Tech. of China
Wang, Lihua, City Univ. of Hong Kong
Ip, Horace,
Zhou, Xuehai, Univ. of Science and Tech. of China
The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called
spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with
the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially
connected joints in the articulated skeletal structure, as well as the temporal dependency due to the continuous movement
of each of the joints. The spatial-temporal contexts of these joints are learnt from the sequences of joints movements and
captured by our ST-HMM. Results of recognizing 11 different action classes on a large number of motion capture sequences
as well as synthetic tracking data show that our approach outperforms traditional HMM approach in terms of robustness
and recognition rates.
16:00-16:20, Paper WeCT1.2
Matching Groups of People by Covariance Descriptor
Cai, Yinghao, Univ. of Oulu
Takala, Valtteri, Univ. of Oulu
Pietikäinen, Matti, Univ. of Oulu
In this paper, we present a new solution to the problem of matching groups of people across multiple non-overlapping
cameras. Similar to the problem of matching individuals across cameras, matching groups of people also faces challenges
such as variations of illumination conditions, poses and camera parameters. Moreover, people often swap their positions
while walking in a group. In this paper, we propose to use covariance descriptor in appearance matching of group images.
Covariance descriptor is shown to be a discriminative descriptor which captures both appearance and statistical properties
of image regions. Furthermore, it presents a natural way of combining multiple heterogeneous features together with a
relatively low dimensionality. Experimental results on two different datasets demonstrate the effectiveness of the proposed
method.
16:20-16:40, Paper WeCT1.3
Boosting Incremental Semi-Supervised Discriminant Analysis for Tracking
Wang, Heng, Chinese Acad. of Sciences
Hou, Xinwen, Chinese Acad. of Sciences
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