Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization
| Autor: | Fernando Bombardelli, Cornelius Hellge, Serhan Gül |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Random field
Markov random field Markov chain Computer science Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Cut Video tracking Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Bitstream Algorithm |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030129385 GCPR |
| DOI: | 10.1007/978-3-030-12939-2_10 |
| Popis: | We propose a method for tracking objects in H.264/AVC compressed videos using a Markov Random Field model. Given an initial segmentation of the target object in the first frame, our algorithm applies a graph-cuts-based optimization to output a binary segmentation map for the next frame. Our model uses only the motion vectors and block coding modes from the compressed bitstream. Thus, complexity and storage requirements are significantly reduced compared to pixel-domain algorithms. We evaluate our method over two datasets and compare its performance to a state-of-the-art compressed-domain algorithm. Results show that we achieve better results in more challenging sequences. |
| Databáze: | OpenAIRE |
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