Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.20
Slip and Fall Events Detection by Analyzing the Integrated Spatiotemporal Energy Map
Huang, Chung-Lin, National Tsing-Hua Univ.
Liao, Tim, National Tsing-Hua Univ.
This paper presents a new method to detect slip and fall events by analyzing the integrated spatiotemporal energy (ISTE)
map. ISTE map includes motion and time of motion occurrence as our motion feature. The extracted human shape is represented
by an ellipse that provides crucial information of human motion activities. We use this features to detect the
events in the video with non-fixed frame rate. This work assumes that the person lies on the ground with very little motion
after the fall accident. Experimental results show that our method is effective for fall and slip detection.
13:30-16:30, Paper TuBCT8.21
Color Constancy using Standard Deviation of Color Channels
Choudhury, Anustup, Univ. of Southern California
Medioni, Gerard, Univ. of Southern California
We address here the problem of color constancy and propose a new method to achieve color constancy based on the statistics
of images with color cast. Images with color cast have standard deviation of one color channel significantly different
from that of other color channels. This observation is also applicable to local patches of images and ratio of the maximum
and minimum standard deviation of color channels of local patches is used as a prior to select a pixel color as illumination
color. We provide extensive validation of our method on commonly used datasets having images under varying illumination
conditions and show our method to be robust to choice of dataset and at least as good as current state-of-the-art color constancy
approaches.
13:30-16:30, Paper TuBCT8.22
Recognizing Human Actions using Key Poses
Baysal, Sermetcan, Bilkent Univ.
Kurt, Mehmet Can, Bilkent Univ.
Duygulu, Pinar, Bilkent Univ.
In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition.
In contrast to the other studies using complex action representations, we propose a simple method, which relies on
extracting key poses from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a
collection of line-pairs, we propose a matching scheme between two frames to compute their similarity. Secondly, to
extract key poses for each action, we present an algorithm, which selects the most representative and discriminative poses
from a set of candidates. Our experimental results on KTH and Weizmann datasets have shown that pose information by
itself is quite effective in grasping the nature of an action and sufficient to distinguish one from others.
13:30-16:30, Paper TuBCT8.23
Action Recognition using Three-Way Cross Correlations Feature of Local Motion Attributes
Matsukawa, Tetsu, Univ. of Tsukuba
Kurita, Takio, National Inst. of Advanced Industrial Science andTechnology
This paper proposes a spatio-temporal feature using three-way cross-correlations of local motion attributes for action
recognition. Recently, the cubic higher-order local auto-correlations (CHLAC) feature has been shown high classification
performances for action recognition. In previous researches, CHLAC feature was applied to binary motion image sequences
that indicates moving or static points. However, each binary motion image lost informations about the type of motion such
as timing of change or motion direction. Therefore, we can improve the classification accuracy further by extending
CHLAC to multivalued motion image sequences that considered several types of local motion attributes. The proposed
method is also viewed as an extension of popular bag-of-features approach. Experimental results using two datasets shows
proposed method outperformed CHLAC features and bag-of-features approach.
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