Transition Hough forest for trajectory-based action recognition

Guillermo Garcia-hernando, Hyung Jin Chang, Ismael Serrano, Oscar Deniz, Tae-kyun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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 languageEnglish
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781509006410
Publication statusPublished - 7 Mar 2016
Event2016 IEEE Winter Conference on Applications of Computer Vision (WACV) - Lake Placid, NY, USA, Lake Placid, NY, United States
Duration: 7 Mar 201610 Mar 2016


Conference2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Country/TerritoryUnited States
CityLake Placid, NY


Dive into the research topics of 'Transition Hough forest for trajectory-based action recognition'. Together they form a unique fingerprint.

Cite this