Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought t...
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Karlsruhe
KIT Scientific Publishing
2020
|
| Series: | Karlsruher Schriften zur Anthropomatik
|
| Subjects: | |
| Online Access: | |
| Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
| Summary: | This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together. |
|---|---|
| Item Description: | Creative Commons (cc), by-sa/4.0, http://creativecommons.org/licenses/by-sa/4.0 |
| Physical Description: | 1 electronic resource (228 p.) |
| ISBN: | 9783731510383 1000122541 |