Quality-Driven video analysis for the improvement of foreground segmentation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones.Fecha de lectura: 15-06-2018It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo

Background initialization for the task of video-surveillance

In this work, we propose a region-wise and batch processing approach for background initialization in video-surveillance based on a spatio-temporal analysis. First, the related work has been explored. Then, the efforts are focused on developing a new background initialization approach to outperform the literature performance. To this end, a temporal analysis and a spatial analysis are performed. In the first stage, we use a previous work techniques adding motion information to increase performance. In the second stage, a multipath iterative reconstruction scheme is performed to build the true background under the assumption of background smoothness, i.e. the empty scene is smoother than the scene with foreground regions. Finally, the results over challenging video-surveillance sequences show the quality of the proposed approach against related work

Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. Ortego, J. C. SanMiguel and J. M. Martínez, "Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection," in IEEE Signal Processing Letters, vol. 22, no. 12, pp. 2368-2372, Dec. 2015. doi: 10.1109/LSP.2015.2482598We present a block-wise approach to detect stationary objects based on spatio-Temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short-and long-Term operation while overcoming challenging issues.This work was partially supported by the Spanish Government (HA-Video TEC2014-5317-R) and by the TEC department (UAM)

Rejection based multipath reconstruction for background estimation in video sequences with stationary objects

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, VOL147 (2016) DOI 10.1016/j.cviu.2016.03.012Background estimation in video consists in extracting a foreground-free image from a set of training frames. Moving and stationary objects may affect the background visibility, thus invalidating the assumption of many related literature where background is the temporal dominant data. In this paper, we present a temporal-spatial block-level approach for background estimation in video to cope with moving and stationary objects. First, a Temporal Analysis module obtains a compact representation of the training data by motion filtering and dimensionality reduction. Then, a threshold-free hierarchical clustering determines a set of candidates to represent the background for each spatial location (block). Second, a Spatial Analysis module iteratively reconstructs the background using these candidates. For each spatial location, multiple reconstruction hypotheses (paths) are explored to obtain its neighboring locations by enforcing inter-block similarities and intra-block homogeneity constraints in terms of color discontinuity, color dissimilarity and variability. The experimental results show that the proposed approach outperforms the related state-of-the-art over challenging video sequences in presence of moving and stationary objects.This work was partially supported by the Spanish Government (HAVideo, TEC2014-53176-R) and by the TEC department (Universidad Autónoma de Madrid)