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1. Object tracking across non-overlapping views by learning inter-camera transfer models.

Author

Chen, Xiaotang, Huang, Kaiqi, and Tan, Tieniu

Subjects
*OBJECT tracking (Computer vision), *MACHINE learning, *ALGORITHMS, *PROBLEM solving, *ESTIMATION theory, *TOPOLOGY
Abstract

Abstract: In this paper, we introduce a novel algorithm to solve the problem of object tracking across multiple non-overlapping cameras by learning inter-camera transfer models. The transfer models are divided into two parts according to different kinds of cues, i.e. spatio-temporal cues and appearance cues. To learn spatio-temporal transfer models across cameras, an unsupervised topology recovering approach based on N-neighbor accumulated cross-correlations is proposed, which estimates the topology of a non-overlapping multi-camera network. Different from previous methods, the proposed topology recovering method can deal with large amounts of data without considering the size of time window. To learn inter-camera appearance transfer models, a color transfer method is used to model the changes of color characteristics across cameras, which has an advantage of low requirements to training samples, making update efficient when illumination conditions change. The experiments are performed on different datasets. Experimental results demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Published
2014
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2. An incremental learning algorithm based on the K-associated graph for non-stationary data classification.

Author

Bertini, João Roberto, Zhao, Liang, and Lopes, Alneu A.

Subjects
*MACHINE learning, *ALGORITHMS, *GRAPH theory, *DATA analysis, *PROBLEM solving, *PROBABILITY theory, *ESTIMATION theory, *DYNAMICAL systems
Abstract

Abstract: Non-stationary classification problems concern the changes on data distribution over a classifier lifetime. To face this problem, learning algorithms must conciliate essential, but difficult to gather, attributes like good classification performance, stability and low associated costs, like processing time and memory. This paper presents an extension of the K-associated optimal graph learning algorithm to cope with classification over non-stationary domains. The algorithm relies on a graph structure consisting of many disconnected components (subgraphs). Such graph enhances data representation by fitting locally groups of data according to a purity measure, which, in turn, quantifies the overlapping between vertices of different classes. As a result, the graph can be used to accurately estimate the probability of unlabeled data to belong to a given class. The proposed algorithm is benefited from the dynamical evolution of the graph by updating its set of components when new data is presented along time, by removing old components as new components arise. Experimental results on artificial and real domains and further statistical analysis show that the proposed algorithm is an effective solution to non-stationary classification problems. [Copyright &y& Elsevier]

Published
2013
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