Abstract
We propose an effective combination of discriminative and generative tracking approaches in order to take the benefits from both. Our algorithm exploits the discriminative properties of Faster R-CNN which helps to generate target specific region proposals. A new proposal distribution is formulated to incorporate information from the dynamic model of moving objects and the detection hypotheses generated by deep learning. We construct the generative appearance model from the region proposals and perform tracking through sequential Bayesian filtering by variable rate color particle filtering (VRCPF). Test results reported on CVPR2013 benchmarking data set demonstrate that the interleaving of tracker and detector enables us to effectively update the target distribution that significantly improves robustness to illumination changes, scale changes, high motion and occlusion.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 3665-3669 |
Number of pages | 5 |
ISBN (Electronic) | 9781509021758 |
DOIs | |
Publication status | Published - 2 Jul 2017 |
Event | 24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 17 Sept 2017 → 20 Sept 2017 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
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Volume | 2017-September |
ISSN (Print) | 1522-4880 |
Conference
Conference | 24th IEEE International Conference on Image Processing, ICIP 2017 |
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Country/Territory | China |
City | Beijing |
Period | 17/09/17 → 20/09/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Deep learning
- Object tracking
- Particle filtering