Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization

Fernando Bombardelli; Serhan Gül; Cornelius Hellge

doi:10.1007/978-3-030-12939-2_10

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Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization

Fernando Bombardelli Serhan Gül Cornelius Hellge

Year: 2019 Lecture notes in computer science Pages: 127-139 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-12939-2_10

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Keywords:

Computer science Markov random field Bitstream Artificial intelligence Cut Segmentation Computer vision Video tracking Markov chain Pixel Coding (social sciences) Graph Frame (networking) Domain (mathematical analysis) Algorithm Image segmentation Object (grammar) Theoretical computer science Mathematics Decoding methods Machine learning

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Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Video Coding and Compression Technologies

Physical Sciences → Computer Science → Signal Processing

Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization

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