Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.12
Eigenbubbles: An Enhanced Apparent BRDF Representation
Kumar, Ritwik, Harvard Univ.
Baba, Vemuri, Univ. of Florida
Banerjee, Arunava, Univ. of Florida
In this paper we address the problem of relighting faces in presence of cast shadows and specularities. We present a solution
to this problem by capturing the spatially varying Apparent Bidirectional Reflectance Functions (ABRDF) fields of human
faces using Spline Modulated Spherical Harmonics and representing them using a few salient spherical functions called
Eigenbubbles. Through extensive experiments on the Extended Yale B and the CMU PIE benchmark datasets we demonstrate
that the proposed method clearly outperforms the state-of-the-art techniques in synthesized image quality. Furthermore,
we show that our framework allows for ABDRF field compression and can also be used to enhance performance of
face recognition algorithms.
13:30-16:30, Paper TuBCT8.13
Reactive Object Tracking with a Single PTZ Camera
Al Haj, Murad, Univ. Autonoma de Bracelona
Bagdanov, Andrew D., Univ. Autonoma de Barcelona
Gonzalez, Jordi, Centre de Visio per Computador
Roca, F. Xavier, Univ. Autonoma de Barcelona
In this paper we describe a novel approach to reactive tracking of moving targets with a pan-tilt-zoom camera. The approach
uses an extended Kalman filter to jointly track the object position in the real world, its velocity in 3D and the camera intrinsics,
in addition to the rate of change of these parameters. The filter outputs are used as inputs to PID controllers which
continuously adjust the camera motion in order to reactively track the object at a constant image velocity while simultaneously
maintaining a desirable target scale in the image plane. We provide experimental results on simulated and real
tracking sequences to show how our tracker is able to accurately estimate both 3D object position and camera intrinsics
with very high precision over a wide range of focal lengths.
13:30-16:30, Paper TuBCT8.14
An Experimental Study of Image Components and Data Metrics for Illumination-Robust Variational Optical Flow
Chetverikov, Dmitry, MTA SZTAKI
Molnar, Jozsef, ELTE
Illumination-robust optical flow algorithms are needed in numerous machine vision applications such as vision-based intelligent
vehicles, surveillance and traffic monitoring. Recently, we have proposed an implicit nonlinear scheme for variational
optical flow that assumes no particular analytical form of energy functional and can accommodate various image
components and data metrics. Using test data with brightness and colour illumination changes, we study different features
and metrics and demonstrate that cross-correlation is superior to the L1 metric for all combinations of the features.
13:30-16:30, Paper TuBCT8.15
Multiple Human Tracking based on Multi-View Upper-Body Detection and Discriminative Learning
Xing, Junliang, Tsinghua Univ.
Ai, Haizhou, Tsinghua Univ. China
Lao, Shihong, OMRON Corp.
This paper focuses on the problem of tracking multiple humans in dense environments which is very challenging due to
recurring occlusions between different humans. To cope with the difficulties it presents, an offline boosted multi-view
upper-body detector is used to automatically initialize a new human trajectory and is capable of dealing with partial human
occlusions. What is more, an online learning process is proposed to learn discriminative human observations, including
discriminative interest points and color patches, to effectively track each human when even more occlusions occur. The
offline and online observation models are neatly integrated into the particle filter framework to robustly track multiple
highly interactive humans. Experiments results on CAVIAR dataset as well as many other challenging real-world cases
demonstrate the effectiveness of the proposed method.
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