Abstract
Moving objects detection in video is essential for many computer vision applications and it is considered as a challenging research issue due to dynamic changes in object size, shape, complex background and illumination changes. In this research article a novel method to detect moving objects in video is proposed grounded on Locality Preserving Projections (LPP) and also thorough analysis of variations in neighbor mode and weight mode for constructing adjacency graph is given. LPP is an unsupervised subspace learning approach used for dimensionality reduction, which preserves the neighborhood structure of dataset while detection, which is vital in further steps accurate tracking and recognition of objects. The proposed method is tested on standard datasets with complex environments and experimental results obtained with variations in neighborhood modes and weight modes are encouraging.
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