Abstract book (pdf) - ICPR 2010

WeBT1 Marmara Hall
Tracking and Surveillance - III Regular Session
Session chair: Liao, Mark (Univ. of Southampton)
13:30-13:50, Paper WeBT1.1
Object Tracking by Structure Tensor Analysis
Donoser, Michael, Graz Univ. of Tech.
Kluckner, Stefan, Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Covariance matrices have recently been a popular choice for versatile tasks like recognition and tracking due to their powerful
properties as local descriptor and their low computational demands. This paper outlines similarities of covariance matrices
to the well-known structure tensor. We show that the generalized version of the structure tensor is a powerful descriptor and
that it can be calculated in constant time by exploiting the properties of integral images. To measure the similarities between
several structure tensors, we describe an approximation scheme which allows comparison in a Euclidean space. Such an approach
is also much more efficient than the common, computationally demanding Riemannian Manifold distances. Experimental
evaluation proves the applicability for the task of object tracking demonstrating improved performance compared to
covariance tracking.
13:50-14:10, Paper WeBT1.2
Prototype Learning using Metric Learning based Behavior Recognition
Zhu, Pengfei, Chinese Acad. of Sciences
Hu, Weiming, Chinese Acad. of Sciences
Yuan, Chunfeng, Chinese Acad. of Sciences
Li, Li, Chinese Acad. of Sciences
Behavior recognition is an attractive direction in the computer vision domain. In this paper, we propose a novel behavior
recognition method based on prototype learning using metric learning. Prototype learning algorithm can improve the classification
performance of nearest-neighbor classifier, reduce the storage and computation requirements. And the metric learning
algorithm is used to advance the performance of the prototype learning. In this paper, We use a kind of compound feature
including local feature and motion feature to recognize human behaviors. The experimental results show the effectiveness
of our method.
14:10-14:30, Paper WeBT1.3
Are Correlation Filters Useful for Human Action Recognition?
Ali, Saad, Carnegie Mellon Univ.
Lucey, Simon, CSIRO
It has been argued in recent work that correlation filters are attractive for human action recognition from videos. Motivation
for their employment in this classification task lies in their ability to: (i) specify where the filter should peak in contrast to
all other shifts in space and time, (ii) have some degree of tolerance to noise and intra-class variation (allowing learning
from multiple examples), and (iii) can be computed deterministically with low computational overhead. Specifically, Maximum
Average Correlation Height (MACH) filters have exhibited encouraging results~\cite{Mikel} on a variety of human
action datasets. Here, we challenge the utility of correlation filters, like the MACH filter, in these circumstances. First, we
demonstrate empirically that identical performance can be attained to the MACH filter by simply taking the~\emph{average}
of the same action specific training examples. Second, we characterize theoretically and empirically under what circumstances
a MACH filter would become equivalent to the average of the action specific training examples. Based on this characterization,
we offer an alternative type of filter, based on a discriminative paradigm, that circumvent the inherent limitations of
correlation filters for action recognition and demonstrate improved action recognition performance.
14:30-14:50, Paper WeBT1.4
Tracking Hand Rotation and Grasping from an IR Camera using Cylindrical Manifold Embedding
Lee, Chan-Su, Yeungnam Univ.
Park, Shin Won, Yeungnam Univ.
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