Abstract book (pdf) - ICPR 2010

gaze estimation improved by 61:06% horizontally and 52:23% vertically with respect to the gaze estimation obtained by
the head pose only. A user study shows the potential of the proposed system.
09:00-11:10, Paper ThAT9.57
Discrimination of Moderate and Acute Drowsiness based on Spontaneous Facial Expressions
Vural, Esra, Univ. of California San Diego
Bartlett, Marian Stewart, Univ. of California San Diego
Littlewort, Gwen, Univ. of California San Diego
Cetin, Mujdat, Sabanci Univ.
Ercil, Aytul, Sabanci Univ.
Movellan, Javier, Univ. of California San Diego
It is important for drowsiness detection systems to identify different levels of drowsiness and respond appropriately at
each level. This study explores how to discriminate moderate from acute drowsiness by applying computer vision techniques
to the human face. In our previous study, spontaneous facial expressions measured through computer vision techniques
were used as an indicator to discriminate alert from acutely drowsy episodes. In this study we are exploring which
facial muscle movements are predictive of moderate and acute drowsiness. The effect of temporal dynamics of action
units on prediction performances is explored by capturing temporal dynamics using an over complete representation of
temporal Gabor Filters. In the final system we perform feature selection to build a classifier that can discriminate moderate
drowsy from acute drowsy episodes. The system achieves a classification rate of .96 A’ in discriminating moderately
drowsy versus acutely drowsy episodes. Moreover the study reveals new information in facial behavior occurring during
different stages of drowsiness.
11:10-12:10, ThPL1 Anadolu Auditorium
J.K. Aggarwal Prize Lecture:
Scene and Object Recognition in Context
Antonio Torralba Plenary Session
Computer Science and Artificial Intelligence Laboratory
Dept. of Electrical Engineering and Computer Science
MIT, USA
Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been
much progress and there are already object recognition systems operating in commercial products. Most of the algorithms
for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions
with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem
by brute force. However, in the real world, objects tend to co-vary with other objects, providing a rich collection of
contextual associations. These contextual associations can be used to reduce the search space by looking only in places in
which the object is expect to be; this also increases performance, by rejecting image patterns that appear to look like the
target object but that are in unlikely places.
As the field moves into integrated systems that try to recognize many object classes and learn about contextual relationships
between objects, the lack of large annotated datasets hinders the fast development of robust solutions. In this talk I will
describe recent work on visual scene understanding that try to build integrated models for scene and object recognition,
emphasizing the power of large database of annotated images in computer vision.
ThBT1 Marmara Hall
Object Detection and Recognition - V Regular Session
Session chair: Wang, Yunhong (Beihang Univ.)
13:30-13:50, Paper ThBT1.1
Finding Multiple Object Instances with Occlusion
Guo, Ge, Chinese Acad. of Sciences
Jiang, Tingting, Peking Univ.
Wang, Yizhou, School of EECS, Peking
Gao, Wen, Peking Univ.
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