Abstract book (pdf) - ICPR 2010

foregrounds in multi-views based on simply selecting segments corresponding to the real foreground in only one image,
further improved foregrounds are extracted by back-projecting 3D objects reconstructed based on the foreground extracted
in the previous step into segments of each image in multi-views. These two steps are iteratively performed until the energy
function is optimized. In the experiments, more accurate boundaries were obtained, although the proposed method used a
simple 3D reconstruction method.
09:00-11:10, Paper ThAT8.42
Human Pose Estimation for Multiple Persons based on Volume Reconstruction
Luo, Xinghan, Utrecht Univ.
Berendsen, Berend
Tan, Robby T., Utrecht Univ.
Veltkamp, R. C., Utrecht Univ.
Most of the development of pose recognition focused on a single person. However, many applications of computer vision
essentially require the estimation of multiple people. Hence, in this paper, we address the problems of estimating poses of
multiple persons using volumes estimated from multiple cameras. One of the main issues that causes the multiple person
from multiple cameras to be problematic is the present of ghost; volumes. This problem arises when the projections of
two different silhouettes of two different persons onto the 3D world overlap in a place where in fact there is no person in
it. To solve this problem, we first introduce a novel principal axis-based framework to estimate the 3D ground plane positions
of multiple people, and then use the position cues to label the multi-person volumes (voxels), while considering
the voxel connectivity. Having labeled the voxels, we fit the volume of each person with a body model, and determine the
pose of the person based on the model. The results on real videos demonstrate the accuracy and efficiency of our approach.
09:00-11:10, Paper ThAT8.43
3D Articulated Shape Segmentation using Motion Information
Kalafatlar, Emre, Koç Univ.
Yemez, Yucel, Koç Univ.
We present a method for segmentation of articulated 3D shapes by incorporating the motion information obtained from
time-varying models. We assume that the articulated shape is given in the form of a mesh sequence with fixed connectivity
so that the inter-frame vertex correspondences, hence the vertex movements, are known a priori. We use different postures
of an articulated shape in multiple frames to constitute an affinity matrix which encodes both temporal and spatial similarities
between surface points. The shape is then decomposed into segments in spectral domain based on the affinity
matrix using a standard K-means clustering algorithm. The performance of the proposed segmentation method is demonstrated
on the mesh sequence of a human actor.
09:00-11:10, Paper ThAT8.44
Online Learning with Self-Organizing Maps for Anomaly Detection in Crowd Scenes
Feng, Jie, Peking Univ.
Zhang, Chao, Peking Univ.
Hao, Pengwei, Queen Mary Univ. of London
Detecting abnormal behaviors in crowd scenes is quite important for public security and has been paid more and more attentions.
Most previous methods use offline trained model to perform detection which can’t handle the constantly changing
crowd environment. In this paper, we propose a novel unsupervised algorithm to detect abnormal behavior patterns in
crowd scenes with online learning. The crowd behavior pattern is extracted from the local spatio-temporal volume which
consists of multiple motion patterns in temporal order. An online self-organizing map (SOM) is used to model the large
number of behavior patterns in crowd. Each neuron can be updated by incrementally learning the new observations. To
demonstrate the effectiveness of our proposed method, we have performed experiments on real-world crowd scenes. The
online learning can efficiently reduce the false alarms while still be able to detect most of the anomalies.
09:00-11:10, Paper ThAT8.45
Scene Classification using Spatial Pyramid of Latent Topics
Ergul, Emrah, Turkish Naval Academy
Arica, Nafiz, Turkish Naval Academy
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