Spatio-contextual Gaussian mixture model for local change detection in underwater video. (1st May 2018)

• Record Type: Journal Article
• Authors: Rout, Deepak Kumar; Subudhi, Badri Narayan; Veerakumar, T.; Chaudhury, Santanu
• Is Part Of: Expert systems with applications. Volume 97(2018)
• Page Start: 117
• Page End: 136
• Publication Date: 2018-05-01
• Subjects: Underwater surveillance -- Wronskian framework -- Object detection -- Background subtraction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.12.009
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: MoG integrated with Wronskian framework for underwater local change detection. Linear dependency of a pixel with background model is tested using Wronskian. Objects can be detected efficiently in blurred and dynamic environment. Adaptive weight updating for background model. Abstract: In this article, a local change detection technique for underwater video sequences is proposed to detect the positions of the moving objects. The proposed change detection scheme integrates the Mixture of Gaussian (MoG) process in a Wronskian framework. It uses spatiotemporal modes (an integration of spatio-contextual and temporal modes) arising over the underwater video sequences to detect the local changes. The Wronskian framework takes care of the spatio-contextual modes whereas MoG models the temporal modes arising due to inter-dependency of a pixel in a video. The proposed scheme follows two steps: background construction and background subtraction. It takes initial few frames to construct a background model and thereby detection of the moving objects in the subsequent frames. During background construction stage; the linear dependency test between the region of supports/ local image patch in the target image frame and the reference background model are carried out using the Wronskian change detection model. The pixel values those are linearly dependent are assumed to be generated from an MoG process and are modeled using the same. Once the background is constructed, then the background subtraction and update process starts from the next frame. The efficiency of the proposed scheme is validated by testing it on two benchmark underwater video databases: fish4knowledge and underwaterchangedetection and one large scale outdoor video database: changedetection.net . The effectiveness of the proposed scheme is demonstrated by comparing it with eighteen state-of-the-art local change detection algorithms. The performance of the proposed scheme is carried out using one subjective and three quantitative evaluation measures.