Abstract book (pdf) - ICPR 2010

fectively captured by supervised trajectory learning. However, it is usually a tough task to obtain a large number of highquality
manually labeled samples in real applications. Thus, how to perform trajectory learning in small training sample
size situations is an important research topic. In this paper, we propose a trajectory learning framework using graph-based
semi-supervised transductive learning, which propagates training sample labels along a particular graph. Furthermore, a
novel trajectory descriptor based on multi-scale key points is proposed to characterize the spatial structural information.
Experimental results demonstrate effectiveness of our framework.
09:00-11:10, Paper ThAT8.27
Detection based Low Frame Rate Human Tracking
Wang, Lu, The Univ. of Hong Kong
Yung, Nelson, the Univ. of Hong Kong
Tracking by association of low frame rate detection responses is not trivial, as motion is less continuous and hence ambiguous.
The problem becomes more challenging when occlusion occurs. To solve this problem, we firstly propose a
robust data association method that explicitly differentiates ambiguous tracklets that are likely to introduce incorrect
linking from other tracklets, and deal with them effectively. Secondly, we solve the long-time occlusion problem by detecting
inter-track relationship and performing track split and merge according to appearance similarity and occlusion
order. Experiment on a challenging human surveillance dataset shows the effectiveness of the proposed method.
09:00-11:10, Paper ThAT8.28
Detecting Dominant Motion Flows in Unstructured/Structured Crowd Scenes
Ozturk, Ovgu, The Univ. of Tokyo
Yamasaki, Toshihiko, The Univ. of Tokyo
Aizawa, Kiyoharu, The Univ. of Tokyo
Detecting dominant motion flows in crowd scenes is one of the major problems in video surveillance. This is particularly
difficult in unstructured crowd scenes, where the participants move randomly in various directions. This paper presents a
novel method which utilizes SIFT features’ flow vectors to calculate the dominant motion flows in both unstructured and
structured crowd scenes. SIFT features can represent the characteristic parts of objects, allowing robust tracking under
non-rigid motion. First, flow vectors of SIFT features are calculated at certain intervals to form a motion flow map of the
video. ‘ext, this map is divided into equally sized square regions and in each region dominant motion flows are estimated
by clustering the flow vectors. Then, local dominant motion flows are combined to obtain the global dominant motion
flows. Experimental results demonstrate the successful application of the proposed method to challenging real-world
scenes.
09:00-11:10, Paper ThAT8.29
Statistical Shape Modeling using Morphological Representations
Velasco-Forero, Santiago, MINES ParisTech
Angulo, Jesus, MINES ParisTech
The aim of this paper is to propose tools for statistical analysis of shape families using morphological operators. Given a
series of shape families (or shape categories), the approach consists in empirically computing shape statistics (i.e., mean
shape and variance of shape) and then to use simple algorithms for random shape generation, for empirical shape confidence
boundaries computation and for shape classification using Bayes rules. The main required ingredients for the present methods
are well known in image processing, such as watershed on distance functions or log-polar transformation. Performance
of classification is presented in a well-known shape database.
09:00-11:10, Paper ThAT8.30
Recovering the Topology of Multiple Cameras by Finding Continuous Paths in a Trellis
Cai, Yinghao, Univ. of Oulu
Kaiqi, Huang, CAS Inst. of Automation
Tan, Tieniu, CAS Inst. of Automation
Pietikäinen, Matti, Univ. of Oulu
In this paper, we propose an unsupervised method for recovering the topology of multiple cameras with non-overlapping
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