Abstract
In this paper, we propose a new discriminative framework based on Hough forests that enables us to efficiently recognize and localize sequential data in the form of spatio-temporal trajectories. Contrary to traditional decision forest-based methods where predictions are made independently of its output temporal context, we introduce the concept of "transition", which enforces the temporal coherence of estimations and further enhances the discrimination between action classes. We start applying our proposed framework to the problem of recognizing and localizing fingertip written trajectories in mid-air using an egocentric camera. To this purpose, we present a new challenging dataset that allows us to evaluate and compare our method with previous approaches. Finally, we apply our framework to general human action recognition using local spatio-temporal trajectories obtaining comparable to state-of-the-art performance on a public benchmark.
Original language | English |
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Title of host publication | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Publisher | IEEE Computer Society |
Number of pages | 8 |
ISBN (Electronic) | 9781509006410 |
DOIs | |
Publication status | Published - 7 Mar 2016 |
Event | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) - Lake Placid, NY, USA, Lake Placid, NY, United States Duration: 7 Mar 2016 → 10 Mar 2016 |
Conference
Conference | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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Country/Territory | United States |
City | Lake Placid, NY |
Period | 7/03/16 → 10/03/16 |