Duration Dependent Codebooks for Change Detection
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.126
Abstract
This paper describes a supervised system for pixel-level change detection for fixed, monocular surveillance cameras. Per-pixel intensity sequences are modeled by a class of Hidden Semi-Markov Models, Duration Dependent Hidden Markov Models (DDHMMs), to accurately account for stocastically periodic phenomena prevalent in real-world video. The per-pixel DDHMMs are used to assign discrete state labels to pixel intensity sequences which summarize the appearance and temporal statistics of the observations. State assignments are then used as a features for constructing per-pixel code books during a training phase to identify changes of interest in new video. The per-pixel intensity model is validated by showing superior predictive performance to pixel representations commonly used in change detection applications. A new data set is presented which contain dynamic, periodic backgrounds with larger time scale variability than previous data sets and the proposed method is compared to state-of-the-art change detection methods using the new videos.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 1.9M)Paper (PDF, 10 pages, 1.8M)
Bibtex File
Citation
Brandon Mayer, and Joseph Mundy. Duration Dependent Codebooks for Change Detection. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.126
title = {Duration Dependent Codebooks for Change Detection},
author = {Mayer, Brandon and Mundy, Joseph},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.126 }
}