Approach to model human appearance based on sparse representation for human tracking in surveillance. Issue 11 (27th July 2020)

• Record Type: Journal Article
• Authors: Damotharasamy, Sangeetha
• Is Part Of: IET image processing. Volume 14:Issue 11(2020)
• Page Start: 2383
• Page End: 2394
• Publication Date: 2020-07-27
• Subjects: object detection -- learning (artificial intelligence) -- image reconstruction -- gradient methods -- image colour analysis -- face recognition -- image sequences -- image motion analysis -- feature extraction -- object tracking -- video signal processing -- image representation; minimal reconstruction error -- target template -- local appearance -- varying illumination environment -- robust tracking algorithm -- fine representation -- sparse code -- image gradient orientation -- local human appearance -- human tracking algorithm -- sparse representation‐based human appearance model; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5961
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In human tracking, sparse representation successfully localises the human in a video with minimal reconstruction error using target templates. However, the state‐of‐the‐art approaches use colour and local appearance of a human to discriminate the human from the background regions, and hence fail when the human is occluded and appears in the varying illumination environment. In this study, a robust tracking algorithm is proposed that utilises gradient orientation and fine and coarse sparse representation of the target template. Sparse representation‐based human appearance model utilises weighted gradient orientation that is insensitive to illumination variation. Coarse and fine representation of sparse code facilitates tracking under varying scales. Subspace learning from image gradient orientation is enforced with occlusion detection during the dictionary updation stage to capture the visual characteristics of the local human appearance that supports tracking under partial occlusion with lesser tracking error. The proposed human tracking algorithm is evaluated on various datasets and shows efficient human tracking performance when compared to the other state‐of‐the‐art approaches. Furthermore, the proposed human tracking algorithm is suitable for surveillance applications.