Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper ThAT8.49
Visibility of Multiple Cameras in a Scene with Unknown Geometry
Zhang, Liuxin, Beijing Inst. of Tech.
Jia, Yunde, Beijing Inst. of Tech.
In this paper, we investigate the problem of determining the visible regions of multiple cameras in a 3D scene without a
priori knowledge of the scene geometry. Our approach is based on a variational energy functional where both the unresolved
visibility information of multiple cameras and the unknown scene geometry are included. We cast visibility estimation
and scene geometry reconstruction as an optimization of the variational energy functional amenable for minimization with
the Euler-Lagrange driven evolution. Starting from any initial value, the accurate visibility of multiple cameras as well as
the true scene geometry can be obtained at the end of the evolution. Experimental results show the validity of our approach.
09:00-11:10, Paper ThAT8.50
Low-Level Image Segmentation based Scene Classification
Akbas, Emre, Univ. of Illinois
Ahuja, Narendra, Univ. of Illinois
This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a
method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the
relative measure of the information content of our features, we compare the results of classifications we obtain using them
with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation
based features, we model the image in terms of a probability density function, a Gaussian mixture model (GMM) to be
specific, of its region features. This GMM is fit to the image by adapting a universal GMM which is estimated so it fits
all images. Adaptation is done using a maximum-aposteriori criterion. We use kernelized versions of Bhattacharyya distance
to measure the similarity between two GMMs and support vector machines to perform classification. We outperform previously
reported results on a publicly available scene classification dataset. These results suggest further experimentation
in evaluating the promise of low level segmentation in image classification.
09:00-11:10, Paper ThAT8.51
Learning Scene Semantics using Fiedler Embedding
Liu, Jingen, Univ. of Michigan
Ali, Saad, Carnegie Mellon Univ.
We propose a framework to learn scene semantics from surveillance videos. Using the learnt scene semantics, a video analyst
can efficiently and effectively retrieve the hidden semantic relationship between homogeneous and heterogeneous
entities existing in the surveillance system. For learning scene semantics, the algorithm treats different entities as nodes
in a graph, where weighted edges between the nodes represent the “initial” strength of the relationship between entities.
The graph is then embedded into a k-dimensional space by Fiedler Embedding.
09:00-11:10, Paper ThAT8.52
Counting Vehicles in Highway Surveillance Videos
Tamersoy, Birgi, The Univ. of Texas at Austin
Aggarwal, J. K., The Univ. of Texas at Austin
This paper presents a complete system for accurately and efficiently counting vehicles in a highway surveillance video.
The proposed approach employs vehicle detection and tracking modules. In the detection module, an automatically trained
binary classifier detects vehicles while providing robustness against view-point, poor quality videos and clutter. Efficient
tracking is then achieved by a simplified multi-hypothesis approach. First an over-complete set of tracks is created considering
every observed detection within a time interval. As needed, hypothesized detections are generated to force continuous
tracks. Finally, a scoring function is used to separate the valid tracks in the over-complete set. Our tracking system
achieved accurate results in significantly challenging highway surveillance videos.
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