Abstract book (pdf) - ICPR 2010

A novel wire detection algorithm for use by unmanned aerial vehicles (UAV) in low altitude urban reconnaissance is presented.
This is of interest to urban search and rescue and military reconnaissance operations. Detection of wires plays an
important role, because thin wires are hard to discern by tele-operators and automated systems. Our algorithm is based on
identification of linear patterns in images. Most existing methods that search for linear patterns use a simple model of a
line, which does not take into account the line surroundings. We propose the use of a robust Gaussian model to approximate
the intensity profile of a line and its surroundings which allows effective discrimination of wires from other visually similar
linear patterns. The algorithm is able to cope with highly cluttered urban backgrounds, moderate rain, and mist. Experimental
results show a 17.7% detection improvement over the baseline.
15:00-17:10, Paper MoBT8.41
Abandoned Objects Detection based on Radial Reach Correlation of Double Illumination Invariant Foreground Masks
Li, Xunli, Peking Univ.
Zhang, Chao, Peking Univ.
Zhang, Duo,
This paper proposes an automatic and robust method to detect and recognize the abandoned objects for video surveillance
systems. Two Gaussian Mixture Models(Long-term and Short-term models) in the RGB color space are constructed to
obtain two binary foreground masks. By refining the foreground masks through Radial Reach Filter(RRF) method, the influence
of illumination changes is greatly reduced. The height/width ratio and a linear SVM classifier based on HOG (Histogram
of Oriented Gradient) descriptor is also used to recognize the left-baggage. Tests on datasets of PETS2006,
PETS2007 and our own videos show that the proposed method in this paper can detect very small abandoned objects
within low quality surveillance videos, and it is also robust to the varying illuminations and dynamic background.
15:00-17:10, Paper MoBT8.42
Unsupervised Visual Object Categorisation via Self-Organisation
Kinnunen, Juha Teemu Ensio, Lappeenranta Univ. of Tech.
Kamarainen, Joni-Kristian, Lappeenranta Univ. of Tech.
Lensu, Lasse, Lappeenranta Univ. of Tech.
Kalviainen, Heikki, Lappeenranta Univ. of Tech.
Visual object categorisation (VOC) has become one of the most actively investigated topic in computer vision. In the
mainstream studies, the topic is considered as a supervised problem, but recently, the ultimate challenge has been posed:
Unsupervised visual object categorisation. Hitherto only a few methods have been published, all of them being computationally
demanding successors of their supervised counterparts. In this study, we address this problem with a simple and
effective method: competitive learning leading to self organisation (self-categorisation). The unsupervised competitive
learning approach is implemented using the Kohonen self-organising map algorithm (SOM). The SOM is used to perform
the both unsupervised codebook generation and object categorisation. We present our method in detail and compare results
to the supervised approach.
15:00-17:10, Paper MoBT8.43
A Novel Shape Feature for Fast Region-Based Pedestrian Recognition
Shahrokni, Ali, Univ. of Reading
Gawley, Darren, Univ. of Adelaide
Ferryman, James, Univ. of Reading
A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised
Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract
representation of the scene. In particular we address the problem of human detection using an over segmented input image.
We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set
of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential
to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people
detection using a pedestrian dataset.
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