3 research outputs found
Multi-view Facial Landmark Detection
In this thesis, we tackle the problem of designing a multi-view facial landmark detector
which is robust and works in real-time on low-end hardware. Our landmark detector
is an instance of the structured output classi ers describing the face by a mixture
of tree based Deformable Part Models (DPM). We propose to learn parameters of
the detector by the Structured Output Support Vector Machine algorithm which, in
contrast to existing methods, directly optimizes a loss function closely related to the
standard evaluation metrics used in landmark detection. We also propose a novel
two-stage approach to learn the multi-view landmark detectors, which provides better
localization accuracy and signi cantly reduces the overall learning time. We propose
several speedups that enable to use the globally optimal prediction strategy based on
the dynamic programming in real time even for dense landmark sets. The empirical
evaluation shows that the proposed detector is competitive with the current state-ofthe-
art both regarding the accuracy and speed.
We also propose two improvements of the Bundle Method for Regularized Risk Minimization
(BMRM) algorithm which is among the most popular batch solvers used
in structured output learning. First, we propose to augment the objective function
by a quadratic prox-center whose strength is controlled by a novel adaptive strategy
preventing zig-zag behavior in the cases when the genuine regularization term is weak.
Second, we propose to speed up convergence by using multiple cutting plane models
which better approximate the objective function with minimal increase in the computational
cost. Experimental evaluation shows that the new BMRM algorithm which
uses both improvements speeds up learning up to an order of magnitude on standard
computer vision benchmarks, and 3 to 4 times when applied to the learning of the
DPM based landmark detector.
vKatedra kybernetik
Artificial Dummies for Urban Dataset Augmentation
Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are too difficult to capture due to safety reasons, or they are very unlikely to happen. The strict safety requirements in assisted and autonomous driving applications call for an extra high detection accuracy also in these rare situations. Having the ability to generate people images in arbitrary poses, with arbitrary appearances and embedded in different background scenes with varying illumination and weather conditions, is a crucial component for the development and testing of such applications.
The contributions of this paper are three-fold.
First, we describe an augmentation method for the controlled synthesis of urban scenes containing people, thus producing rare or never-seen situations. This is achieved with a data generator (called DummyNet) with disentangled control of the pose, the appearance, and the target background scene.
Second, the proposed generator relies on novel network architecture and associated loss that takes into account the segmentation of the foreground person and its composition into the background scene.
Finally, we demonstrate that the data generated by our DummyNet improve the performance of several existing person detectors across various datasets as well as in challenging situations, such as night-time conditions, where only a limited amount of training data is available.
In the setup with only day-time data available, we improve the night-time detector by 17% log-average miss rate over the detector trained with the day-time data only
Analýza biodiverzity v CHKO Bílé Karpaty jako podklad pro stanovení nové zonace a vhodného managementu cenných území
Cílem projektu je zpracovat návrh nové zonace CHKO Bílé Karpaty včetně optimální péče o nejcennější území na základě síťového mapování cévnatých rostlin a vybraných zoologických taxonů. Výsledkem projektu bude i databáze nálezů s grafickými výstupy, mapa kvality vodotečí a vědecké publikace. Zpráva popisuje práce vykonané v roce 2003: Charakteristika CHKO a BR Bílé Karpaty, síťové mapování cévnatých rostlin, ornitologický průzkum, ichtyologický a hydrobiologický průzkum, lepidopterologický průzkum, carabidologický průzkum