Superpixel-Based Causal Multisensor Video Fusion

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 6; pp. 1263 - 1272
• Main Authors: Gangapure, Vijay N., Nanda, Susmit, Chowdhury, Ananda S.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Camouflage; Causal video fusion; Closed circuit television; Eigenvalues and eigenfunctions; Image processing; Military technology; multisensor data; Real time; Real-time systems; saliency models; Smoke; Streaming media; superpixel; Surveillance; Surveillance systems; Three-dimensional displays; Transforms; Video; Video surveillance
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2017.2662743

Video surveillance systems have become extremely important recently. It has been observed that information extracted from a single spectrum video is often insufficient in adverse conditions like low illumination, shadowing, smoke, dust, unstable background, and camouflage. Real-time video processing systems also need to be very fast where future frames are often unavailable at the time of processing the current frame. In this paper, we propose a superpixel-based causal multisensor video fusion algorithm suitable for real-time surveillance tasks. We develop new superpixel level spatial and temporal saliency models. Novel superpixel level multiple fusion rules are also designed to obtain the fused output. Comprehensive comparisons with several existing works clearly indicate the benefit of our solution.