Detection of moving objects using incremental connectivity outlier factor algorithm

Abstract

In this paper, we describe a technique for detection of moving objects in RGB and infra-red (IR) videos. The technique is based on novel incremental connectivity-based outlier factor (IncCOF). The main idea of the proposed approach is to detect moving blocks as outliers-objects dissimilar to objects in their vicinity-within a properly defined feature space. As the feature space, we use representation of videos by spatial-temporal blocks combined with principal component analysis for dimensionality reduction. Experimental evaluation of the proposed approach on a variety of test videos, including PETS repository, demonstrates its applicability and robustness on the choice of parameters.

Robust tracking of multiple objects in video by adaptive fusion of subband particle filters

“…compressive sensing [58]; (ix) outlier detection, e.g. local outlier factor [59]. Finally, (x) transform domain models, e.g.…”