Title
Selection of negative samples and two-stage combination of multiple features for action detection in thousands of videos
Author
Burghouts, G.J.
Schutte, K.
Bouma, H.
den Hollander, R.J.M.
Publication year
2013
Abstract
In this paper, a system is presented that can detect 48 human actions in realistic videos, ranging from simple actions such as ‘walk’ to complex actions such as ‘exchange’. We propose a method that gives a major contribution in performance. The reason for this major improvement is related to a different approach on three themes: sample selection, twostage classification, and the combination of multiple features. First, we show that the sampling can be improved by smart selection of the negatives. Second, we show that exploiting all 48 actions’ posteriors by two-stage classification greatly improves its detection. Third, we show how low-level motion and high-level object features should be combined. These three yield a performance improvement of a factor 2.37 for human action detection in the visint.org test set of 1,294 realistic videos. In addition, we demonstrate that selective sampling and the two-stage setup improve on standard bagof- feature methods on the UT-interaction dataset, and our method outperforms state-of-the-art for the IXMAS dataset
Subject
Physics & Electronics
II - Intelligent Imaging
TS - Technical Sciences
Safety and Security
Informatics
Defence, Safety and Security
Human action detection
Sparse representation
Pose estimation
Interactions between people
Spatiotemporal features
STIP
Tracking of humans
Person detection
Event recognition
Random forest
Support vector machines
To reference this document use:
http://resolver.tudelft.nl/uuid:e8c5c3cf-299c-4a09-87d3-a7307f680c45
DOI
https://doi.org/10.1007/s00138-013-0514-0
TNO identifier
472169
Source
Machine Vision and Applications
Document type
article