JACIII Vol.23 No.4 pp. 705-714
doi: 10.20965/jaciii.2019.p0705


Scalable Change Detection Using Place-Specific Compressive Change Classifiers

Kanji Tanaka

University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

January 20, 2018
February 1, 2019
July 20, 2019
place-specific change classifiers, scalable change detection, zero-shot learning, bag-of-words scene model
Scalable Change Detection Using Place-Specific Compressive Change Classifiers

Scalable change detection for long-term map maintenance

With recent progress in large-scale map maintenance and long-term map learning, the task of change detection on a large-scale map from a visual image captured by a mobile robot has become a problem of increasing criticality. In this paper, we present an efficient approach of change-classifier-learning, more specifically, in the proposed approach, a collection of place-specific change classifiers is employed. Our approach requires the memorization of only training examples (rather than the classifier itself), which can be further compressed in the form of bag-of-words (BoW). Furthermore, through the proposed approach the most recent map can be incorporated into the classifiers by straightforwardly adding or deleting a few training examples that correspond to these classifiers. The proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.

Cite this article as:
K. Tanaka, “Scalable Change Detection Using Place-Specific Compressive Change Classifiers,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.4, pp. 705-714, 2019.
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Last updated on Jul. 01, 2020