Abstract book (pdf) - ICPR 2010

Biswas, Jit, Inst. for Infocomm Res.
Liou, Kou Juch, Industrial Tech. Res. Inst.
Hsia, C. C., ITRI
Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally,
some works address the problem from either pressure sensor or video image. This paper presents a multimodal
approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in
order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in
the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate
that the multimodal approach achieves better performance than the approaches using single modal sensing.
13:30-16:30, Paper ThBCT8.49
Exploiting System Knowledge to Improve ECOC Reject Rules
Simeone, Paolo, Univ. of Cassino
Marrocco, Claudio, Univ. of Cassino
Tortorella, Francesco, Univ. of Cassino
Error Correcting Output Coding is a common technique for multiple class classification tasks which decomposes the original
problem in several two-class problems solved through dichotomizers. Such classification system can be improved
with a reject option which can be defined according to the level of information available from the dichotomizers. This
paper analyzes how this knowledge is useful when applying such reject rules. The nature of the outputs, the kind of the
employed classifiers and the knowledge of their loss function are influential details for the improvement of the general
performance of the system. Experimental results on popular benchmark data sets are reported to show the behavior of the
different schemes.
13:30-16:30, Paper ThBCT8.50
Human Smoking Event Detection using Visual Interaction Clues
Wu, Pin, Yuan-Ze University
Hsieh, Jun-Wei, Yuan-Ze University
Cheng, Jiun-Cheng, National Taiwan Ocean Univ.
Cheng, Shyi-Chyi, National Taiwan Ocean Univ.
Tseng, Shau-Yin, Industry Tech. Res. Institute
This paper presents a novel scheme to automatically and directly detect smoking events in video. In this scheme, a colorbased
ratio histogram analysis is introduced to extract the visual clues from appearance interactions between lighted cigarette
and its human holder. The techniques of color re-projection and Gaussian Mixture Models (GMMs) enable the tasks
of cigarette segmentation and tracking over the background pixels. Then, a key problem for event analysis is the nonregular
form of smoking events. Thus, we propose a self-determined mechanism to analyze this suspicious event using
HHM framework. Due to the uncertainties of cigarette size and color, there is no automatic system which can well analyze
human smoking events directly from videos. The proposed scheme is compatible to detect the smoking events of uncertain
actions with various cigarette sizes, colors, and shapes, and has capacity to extend visual analysis to human events of
similar interaction relationship. Experimental results show the effectiveness and real-time performances of our scheme in
smoking event analysis.
13:30-16:30, Paper ThBCT8.51
Malware Detection on Mobile Devices using Distributed Machine Learning
Sharifi Shamili, Ashkan, RWTH Aachen Univ.
Bauckhage, Christian, Fraunhofer IAIS
Alpcan, Tansu, Tech. Univ. Berlin
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware)
on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving
way using a statistical classification model which is evolved by training with examples of both normal usage patterns
and unusual behavior. The system is evaluated using the MIT reality mining data set. The results indicate that the
distributed learning system trains quickly and performs reliably. Moreover, it is robust against failures of individual components.
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