Abstract book (pdf) - ICPR 2010

ance cues. We demonstrate that incorporating the shape prior yields promising performance improvement over temporal
and appearance priors on various object tracking scenarios.
09:00-11:10, Paper ThAT8.57
Real-Time Abnormal Event Detection in Complicated Scenes
Shi, Yinghuan, Nanjing Univ.
Gao, Yang, Nanjing Univ.
Wang, Ruili, Massey Univ.
In this paper, we proposed a novel real-time abnormal event detection framework that requires a short training period
and has a fast processing speed. Our approach is based on phase correlation and our newly developed spatial-temporal
co-occurrence Gaussian mixture models (STCOG)with the following steps: (i) a frame is divided into non-overlapping
local regions; (ii) phase correlation is used to estimate the motion vectors between successive two frames for all corresponding
local regions, and (iii) STCOG is used to model normal events and detect abnormal events if any deviation
from the trained STCOG is found. Our proposed approach is also able to update the parameters incrementally and can
be applied in complicated scenes. The proposed approach outperforms previous ones in terms of shorter training periods
and lower computational complexity.
ThAT9 Lower Foyer
Human Computer Interaction and Biometrics Poster Session
Session chair: Alba Castro, Jose Luis (Univ. of Vigo)
09:00-11:10, Paper ThAT9.1
Encoding Actions via Quantized Vocabulary of Averaged Silhouettes
Wang, Liang, The Univ. of Melbourne
Leckie, Christopher, The Univ. of Melbourne
Human action recognition from video clips has received increasing attention in recent years. This paper proposes a simple
yet effective method for the problem of action recognition. The method aims to encode human actions using the quantized
vocabulary of averaged silhouettes that are derived from space-time windowed shapes and implicitly capture local temporal
motion as well as global body shape. Experimental results on the publicly available Weizmann dataset have demonstrated
that, despite its simplicity, our method is effective for recognizing actions, and is comparable to other state-of-the-art methods.
09:00-11:10, Paper ThAT9.2
Action Recognition using Space-Time Shape Difference Images
Qu, Hao, The Univ. of Melbourne
Wang, Liang, The Univ. of Melbourne
Leckie, Christopher, The Univ. of Melbourne
A common approach to human action recognition is to use 2-D silhouettes in the space-time volume as a basis for further
extraction of useful features. In this paper, we present a novel motion representation based on difference images. We show
that this representation exploits the dynamics of motion, and show its effectiveness in action recognition. Moreover, experimental
results demonstrate that this method is highly accurate and is not sensitive to the resolution of the video.
09:00-11:10, Paper ThAT9.3
A Brain Computer Interface for Communication using Real-Time fMRI
Eklund, Anders, Linköping Univ.
Andersson, Mats, Linköping Univ.
Ohlsson, Henrik, Linköping Univ.
Ynnerman, Anders, Linköping Univ.
Knutsson, Hans,
We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic
resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different
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