Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.16
Visual Tracking using Sparsity Induced Similarity
Liu, Huaping, Tsinghua Univ.
Sun, Fuchun, Tsinghua Univ.
Currently sparse signal reconstruction gains considerable interests and is applied in many fields. In this paper, we propose
a new approach which utilizes the sparsity induced similarity to construct the tracking algorithm. Compared with stateof-the-art,
the advantage of this approach is that the sparse representation needs to be calculated for only once and therefore
the time cost is dramatically decreased. In addition, extensive experimental comparisons show that the proposed approach
is more robust than some existing approaches.
13:30-16:30, Paper TuBCT8.17
An Information Fusion Approach for Multiview Feature Tracking
Ataer-Cansizoglu, Esra, Boston Univ.
Betke, Margrit, Boston Univ.
We propose an information fusion approach to tracking objects from different viewpoints that can detect and recover from
tracking failures. We introduce a reliability measure that is a combination of terms associated with correlation-based template
matching and the epipolar geometry of the cameras. The measure is computed to evaluate the performance of 2D
trackers in each camera view and detect tracking failures. The 3D object trajectory is constructed using stereoscopy and
evaluated to predict the next 3D position of the object. In case of track loss in one camera view, the projection of the predicted
3D position onto the image plane of this view is used to reinitialize the lost 2D tracker. We conducted experiments
with 34 subjects to evaluate our proposed system on videos of facial feature movements during human-computer interaction.
The system successfully detected feature loss and gave promising results on accurate re-initialization of the feature.
13:30-16:30, Paper TuBCT8.18
Monocular 3D Tracking of Deformable Surfaces using Linear Programming
Chenhao, Wang, Shanghai Jiao Tong Univ.
Li, Xiong, Shanghai Jiao Tong Univ.
Liu, Yuncai, Shanghai Jiao Tong Univ.
We present a method for 3D shape reconstruction of inextensible deformable surfaces from monocular image sequences.
The key of our approach is to represent the surface as 3D triangulated mesh and formulate the reconstruction problem as
a sequence of Linear Programming (LP) problems which can be effectively solved. The LP problem consists of data constraints
which are 3D-to-2D keypoint correspondences and shape constraints which prevent large changes of the edge orientation
between consecutive frames. Furthermore, we use a refined bisection algorithm to accelerate the computing speed.
The robustness and efficiency of our approach are validated on both synthetic and real data.
13:30-16:30, Paper TuBCT8.19
Exploiting Visual Quasi-Periodicity for Automated Chewing Event Detection using Active Appearance Models and
Support Vector Machines
Cadavid, Steven, Univ. of Miami
Abdel-Mottaleb, Mohamed, Univ. of Miami
We present a method that automatically detects chewing events in surveillance video of a subject. Firstly, an Active Appearance
Model (AAM) is used to track a subject’s face across the video sequence. It is observed that the variations in the
AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between
chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis
to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality
reduction via spectral regression. The low-dimensional representations of the power spectra are employed
to train a Support Vector Machine (SVM) binary classifier to detect chewing events. Experimental results yielded a cross
validated percentage agreement of 93.4%, indicating that the proposed system provides an efficient approach to automated
chewing detection.
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