Journal ArticleDOI
Spatio-contextual Gaussian mixture model for local change detection in underwater video
TLDR
The effectiveness of the proposed scheme is demonstrated by comparing it with eighteen state-of-the-art local change detection algorithms and the performance is carried out using one subjective and three quantitative evaluation measures.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.read more
Citations
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Journal ArticleDOI
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: In this paper, a survey of background subtraction methods used in real applications is presented, in order to identify the real challenges met in practice, the current used background models and to provide future directions.
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Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: This work identifies the background models that are effectively used in real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions.
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Big data analytics for video surveillance
TL;DR: The state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance are reported here.
Journal ArticleDOI
Walsh–Hadamard-Kernel-Based Features in Particle Filter Framework for Underwater Object Tracking
TL;DR: The proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges, and the performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes.
Journal ArticleDOI
Kernelized Fuzzy Modal Variation for Local Change Detection From Video Scenes
TL;DR: The proposed background subtraction scheme, utilizes the fuzzy modal variation as the cost function for fitting the pixel values of the image frames and provides better results compared to the twenty one existing state-of-the-art techniques.
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