Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection
EntidadUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
EditorInstitute of Electrical and Electronics Engineers Inc.
Fecha de edición2015-12-01
10.1109/LSP.2015.2482598IEEE Signal Processing Letters 22.12 (2015): 2368 – 2372
ISSN1070-9908 (print); 1558-2361 (online)
Financiado porThis work was partially supported by the Spanish Government (HA-Video TEC2014-5317-R) and by the TEC department (UAM).
ProyectoGobierno de España. TEC2014-5317-R
Versión del editorhttp://dx.doi.org/10.1109/LSP.2015.2482598
MateriasAbandoned object; Long-Term; Online clustering; Stability changes; Stationary object detection; Telecomunicaciones
NotaPersonal 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.2015.2482598
Derechos© 2015 IEEE
We 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.
Google Scholar:Ortego Hernández, Diego - San Miguel Avedillo, Juan Carlos - Martínez Sánchez, José María
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