Abstract book (pdf) - ICPR 2010

the backgrounds with random landscape images from Google and by applying random Euclidean transformations to the
foreground objects. We demonstrate how the proposed randomization process makes visual object categorization more
challenging improving the relative results of methods which categorize objects by their visual appearance and are invariant
to pose changes. The new data set is made publicly available for other researchers.
15:00-17:10, Paper MoBT8.52
A Reliability Assessment Paradigm for Automated Video Tracking Systems
Chen, Chung-Hao, North Carolina Central Univ.
Yao, Yi, GE Global Res.
Koschan, Andreas, The Univ. of Tennessee
Abidi, Mongi, The Univ. of Tennessee
Most existing performance evaluation methods concentrate on defining separate metrics over a wide range of conditions
and generating standard benchmarking video sequences for examining the effectiveness of video tracking systems. In
other words, these methods attempt to design a robustness margin or factor for the system. These methods are deterministic
in which a robustness factor, for example, 2 or 3 times the expected number of subjects to track or the strength of illumination
would be required in the design. This often results in over design, thus increasing costs, or under design causing
failure by unanticipated factors. In order to overcome these limitations, we propose in this paper an alternative framework
to analyze the physics of the failure process and, through the concept of reliability, determine the time to failure in automated
video tracking systems. The benefit of our proposed framework is that we can provide a unified and statistical index
to evaluate the performance of automated video tracking system for a task to be performed. At the same time, the uncertainty
problem about a failure process, which may be caused by the systems complexity, imprecise measurements of the
relevant physical constants and variables, or the indeterminate nature of future events, can be addressed accordingly based
on our proposed framework.
15:00-17:10, Paper MoBT8.53
Road Sign Detection in Images: A Case Study
Belaroussi, Rachid, Univ. Paris Est,INRETS-LCPC
Foucher, Philippe, Lab. Des Ponts et Chaussées
Tarel, Jean-Philippe, LCPC
Soheilian, Bahman, Ins. Géographique National,
Charbonnier, Pierre, ERA27 LCPC – LRPC
Paparoditis, Nicolas, Inst. Geographique National
Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled
in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we
focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the
same ground-truth image database, results obtained by three algorithms that sample different state-of-the-art approaches.
The three tested algorithms: Contour Fitting, Radial Symmetry Transform, and pair-wise voting scheme, all use color and
edge information and are based on geometrical models of road signs. The test dataset is made of 847 images 960x1080 of
complex urban scenes (available at www.itowns.fr/benchmarking.html). They feature 251 road signs of different shapes
(circular, rectangular, triangular), sizes and types. The pros and cons of the three algorithms are discussed, allowing to
draw new research perspectives.
15:00-17:10, Paper MoBT8.54
ImageCLEF@ICPR Contest: Challenges, Methodologies and Results of the Photo Annotation Task
Nowak, Stefanie, Fraunhofer Inst. For Digital Media Tech.
The Photo Annotation Task is performed as one task in the Image CLEF@ICPR contest and poses the challenge to annotate
53 visual concepts in Flickr photos. Altogether 12 research teams met the multilabel classification challenge and submitted
solutions. The participants were provided with a training and a validation set consisting of 5,000 and 3,000 annotated images,
respectively. The test was performed on 10,000 images. Two evaluation paradigms have been applied, the evaluation per
concept and the evaluation per example. The evaluation per concept was performed by calculating the Equal Error Rate and
the Area Under Curve (AUC). The evaluation per example utilizes a recently proposed Ontology Score. For the concepts, an
average AUC of 86.5% could be achieved, including concepts with an AUC of 96%. The classification performance for each
image ranged between 59% and 100% with an average score of 85%.
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