Small moving targets detection using outlier detection algorithms

Abstract

Recent research in motion detection has shown that various outlier detection methods could be used for efficient detection of small moving targets. These algorithms detect moving objects as outliers in a properly defined attribute space, where outlier is defined as an object distinct from the objects in its neighborhood. In this paper, we compare the performance of two incremental outlier detection algorithms, namely the incremental connectivity-based outlier factor and the incremental local outlier factor to modified Stauffer-Grimson algorithm. Each video sequence is represented with spatial-temporal blocks extracted from the raw video. Principal component analysis (PCA) is applied on these blocks in order to reduce the dimensionality of extracted data. Extensive experiments performed on several data sets, including infrared sequences from OSU Thermal Pedestrian Database repository, and data collected at Delaware State University from FLIR Systems PTZ cameras have shown promising results in using outlier detection for detection of small moving targets.

Watch-n-patch: Unsupervised understanding of actions and relations

“…The image segmentation provides richer candidate super-pixels rather than pixels to further extracting objects. Second we use a moving targets detection approach [28] to detecting foreground mask by removing the unnecessary steady backgrounds. Third we consider the interactive objects should be close to tracked human hands.…”