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Author |
Ivan Huerta |
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Title |
Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments |
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2010 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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This Thesis is mainly divided in two parts. The first one presents a study of motion
segmentation problems. Based on this study, a novel algorithm for mobile-object
segmentation from a static background scene is also presented. This approach is
demonstrated robust and accurate under most of the common problems in motion
segmentation. The second one tackles the problem of shadows in depth. Firstly, a
bottom-up approach based on a chromatic shadow detector is presented to deal with
umbra shadows. Secondly, a top-down approach based on a tracking system has been
developed in order to enhance the chromatic shadow detection.
In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely
colour, edge and intensity. Our second contribution is a hybrid architecture which
handles the main problems observed in such a case analysis, by fusing (i) the knowledge from these three cues and (ii) a temporal difference algorithm. On the one hand,
we enhance the colour and edge models to solve both global/local illumination changes
(shadows and highlights) and camouflage in intensity. In addition, local information is
exploited to cope with a very challenging problem such as the camouflage in chroma.
On the other hand, the intensity cue is also applied when colour and edge cues are not
available, such as when beyond the dynamic range. Additionally, temporal difference
is included to segment motion when these three cues are not available, such as that
background not visible during the training period. Lastly, the approach is enhanced
for allowing ghost detection. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and
qualitatively demonstrated in the experimental results, by comparing our approach
with most best-known state-of-the-art approaches.
Motion Segmentation has to deal with shadows to avoid distortions when detecting
moving objects. Most segmentation approaches dealing with shadow detection are
typically restricted to penumbra shadows. Therefore, such techniques cannot cope
well with umbra shadows. Consequently, umbra shadows are usually detected as part
of moving objects.
Firstly, a bottom-up approach for detection and removal of chromatic moving
shadows in surveillance scenarios is proposed. Secondly, a top-down approach based
on kalman filters to detect and track shadows has been developed in order to enhance
the chromatic shadow detection. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating
chromatic moving shadows from moving objects.
Well-known colour and gradient models are extended and improved into an invariant colour cone model and an invariant gradient model, respectively, to perform
automatic segmentation while detecting potential shadows. Hereafter, the regions corresponding to potential shadows are grouped by considering ”a bluish effect” and an
edge partitioning. Lastly, (i) temporal similarities between local gradient structures
and (ii) spatial similarities between chrominance angle and brightness distortions are
analysed for all potential shadow regions in order to finally identify umbra shadows.
In the top-down process, after detection of objects and shadows both are tracked
using Kalman filters, in order to enhance the chromatic shadow detection, when it
fails to detect a shadow. Firstly, this implies a data association between the blobs
(foreground and shadow) and Kalman filters. Secondly, an event analysis of the different data association cases is performed, and occlusion handling is managed by a
Probabilistic Appearance Model (PAM). Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their
respective Kalman Filters. From this association several cases are studied, as a result
lost chromatic shadows are correctly detected. Finally, the tracking results are used
as feedback to improve the shadow and object detection.
Unlike other approaches, our method does not make any a-priori assumptions
about camera location, surface geometries, surface textures, shapes and types of
shadows, objects, and background. Experimental results show the performance and
accuracy of our approach in different shadowed materials and illumination conditions. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Gonzalez;Xavier Roca |
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978-84-937261-3-3 |
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ISE @ ise @ Hue2010 |
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1332 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Adaptive Correlation Filters for Pattern Recognition |
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Journal |
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Year |
2006 |
Publication |
Pattern Recognition and Image Analysis |
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16 |
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3 |
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425-431 |
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Keywords |
Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE @ ise @ KMA2006a |
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673 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters |
Type |
Miscellaneous |
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Year |
2006 |
Publication |
Topical Meeting on Optoinformatics / Information Photonics, 150–151 |
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Saint-Petersburg (Russia) |
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ISE @ ise @ KMA2006b |
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674 |
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Author |
V. Kober; Mikhail Mozerov; Josue Albarez; I.A. Ovseyevich |
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Title |
Algorithms for Impulse Noise Renoval from Corrupted Color Images |
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2007 |
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ISE @ ise @ KMA2007 |
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811 |
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Author |
Yuhua Luo; Francisco Jose Perales; Juan J. Villanueva |
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Title |
An automatic Rotoscopy System for Human Motion Based on a Biomedical Graphical Model. |
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Journal Article |
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Year |
1992 |
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Computer & Graphics |
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16 |
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4 |
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355-362 |
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ISE @ ise @ LPV1992 |
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249 |
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Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Title |
Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering |
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2008 |
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Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665 |
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ISE @ ise @ MAR2008c |
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970 |
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Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Title |
Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System |
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2009 |
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Pattern Recognition and Image Analysis |
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19 |
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1 |
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165-171 |
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An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path. |
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1054-6618 |
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ISE @ ise @ MAR2009a |
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1160 |
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Author |
Mikhail Mozerov; Ariel Amato; Xavier Roca |
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Title |
Occlusion Handling in Trinocular Stereo using Composite Disparity Space Image |
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Conference Article |
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2009 |
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19th International Conference on Computer Graphics and Vision |
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69–73 |
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In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) can be merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. A dense disparity map is obtained with a global optimization algorithm using the proposed composite DSI. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. One of the top positions in the rank of the Middlebury website confirms the performance of our method to be competitive with the best stereo matching. |
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Moscow (Russia) |
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978-5-317-02975-3 |
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GRAPHICON |
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ISE @ ise @ MAR2009b |
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1207 |
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Mikhail Mozerov; V. Kober; I.A. Ovseyevich |
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Title |
A Stereo Matching Algorithm with Global Smoothness Criterion |
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Miscellaneous |
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2006 |
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Topical Meeting on Optoinformatics / Information Photonics, 133–135 |
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Saint-Petersburg (Russia) |
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ISE @ ise @ MKO2006 |
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675 |
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Mikhail Mozerov; V. Kober; I.A. Ovseyevich |
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Title |
Robust Dynamic Programming Algorithm for Motion Detection and Estimation |
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2007 |
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ISE @ ise @ MKO2007 |
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810 |
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Mikhail Mozerov; V. Kober |
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Impulse Noise Removal with Gradient Adaptive Neighborhoods |
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2006 |
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Optical Engineering, 45: 67003 |
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ISE @ ise @ MoK2006 |
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676 |
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Mikhail Mozerov |
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An Effective Stereo Matching Algorithm with Optimal Path Cost Aggregation |
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2006 |
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28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 617–626 |
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ISE @ ise @ Moz2006 |
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Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
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Title |
3D Human Motion Sequences Synchronization Using Dense Matching Algorithm |
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2006 |
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28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 485–494, ISBN 978–3–540–44412–1 |
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Berlin (Germany) |
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ISE @ ise @ MRR2006 |
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678 |
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Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
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Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. |
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1110-8657 |
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ISE @ ise @ MRR2010 |
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1208 |
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Author |
Francisco Javier Orozco; Pau Baiget; Jordi Gonzalez; Xavier Roca |
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Eyelids and Face Tracking in Real-Time |
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Miscellaneous |
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2006 |
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6th IASTED International Conference Visualization, Imaging, and Image Processing (VIIP´06), 165–170 |
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Palma de Mallorca (Spain) |
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ISE |
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ISE @ ise @ OBG2006 |
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730 |
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