An Adaptive Motion Model for Person Tracking with Instantaneous Head-Pose Features
| Autor: | Michael J. V. Leach, Neil Robertson, Sankha S. Mukherjee, Rolf H. Baxter |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Computer science
business.industry Applied Mathematics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Kalman filter Tracking (particle physics) Motion (physics) Signal Processing Prior probability Trajectory Computer vision Artificial intelligence Electrical and Electronic Engineering Set (psychology) business Test data |
| Zdroj: | Baxter, R H, Leach, M J V, Mukherjee, S S & Robertson, N M 2015, ' An adaptive motion model for person tracking with instantaneous head-pose features ', IEEE Signal processing Letters, vol. 22, no. 5, pp. 578-582 . https://doi.org/10.1109/LSP.2014.2364458 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2014.2364458 |
| Popis: | This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous `intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data. |
| Databáze: | OpenAIRE |
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