Abstract book (pdf) - ICPR 2010

fields of view. The nodes in the topology graph are defined as entry/exit zones in each camera while the connectivity between
nodes is inferred through finding continuous paths in a trellis where appearance information and temporal information
of moving objects are encoded. Unlike previous methods which assume a single mode transition distribution between
nodes, our method is capable of dealing with multi-modal transition situations when both cars and pedestrians are in the
scene. Results on simulated and real-life datasets demonstrate the effectiveness of the proposed method.
09:00-11:10, Paper ThAT8.31
On-Line Random Naive Bayes for Tracking
Godec, Martin, Graz Univ. of Tech.
Leistner, Christian, Graz Univ. of Tech.
Saffari, Amir, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications.
However, it was shown that the tree structure in Forests can be replaced by even simpler structures, e.g., Random
Naive Bayes classifiers, yielding similar performance. The goal of this paper is to benefit from these findings to develop
an efficient on-line learner. Based on the principals of on-line Random Forests, we adapt the Random Naive Bayes classifier
to the on-line domain. For that purpose, we propose to use on-line histograms as weak learners, which yield much better
performance than simple decision stumps. Experimentally we show, that the approach is applicable to incremental learning
on machine learning datasets. Additionally, we propose to use an iir filtering-like forgetting function for the weak learners
to enable adaptivity and evaluate our classifier on the task of tracking by detection.
09:00-11:10, Paper ThAT8.32
Interest Point based Tracking
Kloihofer, Werner, Center Communication Systems GmbH
Kampel, Martin, Vienna Univ. of Tech.
This paper deals with a novel method for object tracking. In the first step interest points are detected and feature descriptors
around them are calculated. Sets of known points are created, allowing tracking based on point matching. The set representation
is updated online at every tracking step. Our method uses one-shot learning with the first frame, so no offline
and no supervised learning is required. Following an object recognition based approach there is no need for a background
model or motion model, allowing tracking of abrupt motion and with non-stationary cameras. We compare our method to
Mean Shift and Tracking via Online Boosting, showing the benefits of our approach.
09:00-11:10, Paper ThAT8.33
Stochastic Filtering of Level Sets for Curve Tracking
Avenel, Christophe, Irisa
Memin, Etienne
Perez, Patrick
This paper focuses on the tracking of free curves using non-linear stochastic filtering techniques. It relies on a particle
filter which includes color measurements. The curve and its velocity are defined through two coupled implicit level set
representations. The stochastic dynamics of the curve is expressed directly on the level set function associated to the curve
representation and combines a velocity field captured from the additional second level set attached to the past curve’s
points location. The curve’s dynamics combines a low-dimensional noise model and a data-driven local force. We demonstrate
how this approach allows the tracking of highly and rapidly deforming objects, such as convective cells in infra-red
satellite images, while providing a location-dependent assessment of the estimation confidence.
09:00-11:10, Paper ThAT8.34
Scalable Cage-Driven Feature Detection and Shape Correspondence for 3D Point Sets
Seversky, Lee, State Univ. of New York at Binghamton
Yin, Lijun, State Univ. of New York at Binghamton
We propose an automatic deformation-driven correspondence algorithm for 3D point sets of non-rigid articulated shapes.
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