Abstract book (pdf) - ICPR 2010

Liu, Cheng-Lin, Chinese Acad. of Sciences
Tracking is recently formulated as a problem of discriminating the object from its nearby background, where the classifier
is updated by new samples successively arriving during tracking. Depending on whether labeling the samples or not, the
tracker can be designed in a supervised or semi-supervised manner. This paper proposes a novel semi-supervised algorithm
for tracking by combining Semi-supervised Discriminant Analysis (SDA) with an online boosting framework. Using the
local geometric structure information from the samples, the SDA-based weak classifier is made more robust to outliers.
Meanwhile, we design an incremental updating mechanism for SDA so that it can adapt to appearance changes. We further
propose an Extended SDA (ESDA) algorithm, which gives better discrimination ability. Results on several challenging
video sequences demonstrate the effectiveness of the method.
16:40-17:00, Paper WeCT1.4
Optical Rails: View-Based Track Following with Hemispherical Environment Model and Orientation View
Descriptors
Dederscheck, David, Goethe Univ. Frankfurt
Zahn, Martin, Goethe Univ. Frankfurt
Friedrich, Holger, Goethe Univ. Frankfurt
Mester, Rudolf, Goethe Univ. Frankfurt
We present a purely view-based method for robot navigation along a prerecorded track using compact omni directional
view-descriptors. This paper focuses on a new model for the navigation environment to determine the steering direction
by efficient holistic comparison of views. The concept of view descriptors based on low-order expansion of local orientation
vectors into spherical harmonic basis functions is augmented by a linear illumination model, providing discriminative
view matching also under illumination changes.
17:00-17:20, Paper WeCT1.5
Forward-Backward Error: Automatic Detection of Tracking Failures
Kalal, Zdenek, Univ. of Surrey
Mikolajczyk, Krystian, Univ. of Surrey
Matas, Jiri, CTU Prague
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error,
i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured.
We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories
in video sequences. We demonstrate that the approach is complementary to commonly used normalized
cross-correlation (NCC). Based on the error, we propose a novel object tracker called Median Flow. State-of-the-art performance
is achieved on challenging benchmark video sequences which include non-rigid objects.
WeCT2 Topkapı Hall A
Pattern Recognition Systems and Applications - II Regular Session
Session chair: Marinai, Simone (Univ. of Florence)
15:40-16:00, Paper WeCT2.1
Scene-Adaptive Human Detection with Incremental Active Learning
Joshi, Ajay, Univ. of Minnesota, Twin Cities
Porikli, Fatih, MERL
In many computer vision tasks, scene changes hinder the generalization ability of trained classifiers. For instance, a human
detector trained with one set of images is unlikely to perform well in different scene conditions. In this paper, we propose
an incremental learning method for human detection that can take generic training data and build a new classifier adapted
to the new deployment scene. Two operation modes are proposed: i) a completely autonomous mode wherein first few
empty frames of video are used for adaptation, and ii) an active learning approach with user in the loop, for more challenging
scenarios including situations where empty initialization frames may not exist. Results show the strength of the
proposed methods for quick adaptation.
- 204 -