@inproceedings{dea62cbc069249eb9b5af56727ee36f7,
title = "Action detection with improved dense trajectories and sliding window",
abstract = "In this paper we describe an action/interaction detection system based on improved dense trajectories [19], multiple visual descriptors and bag-of-features representation. Given that the actions/interactions are not mutual exclusive, we train a binary classifier for every predefined action/interaction. We rely on a non-overlapped temporal sliding window to enable the temporal localization. We have tested our system in ChaLearn Looking at People Challenge 2014 Track 2 dataset [1,2]. We obtained 0.4226 average overlap, which is the 3rd place in the track of the challenge. Finally, we provide an extensive analysis of the performance of this system on different actions and provide possible ways to improve a general action detection system.",
keywords = "Action detection, Action recognition, Dense trajectories, Video analysis",
author = "Zhixin Shu and Kiwon Yun and Dimitris Samaras",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
year = "2015",
doi = "10.1007/978-3-319-16178-5\_38",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "541--551",
editor = "Bronstein, \{Michael M.\} and Carsten Rother and Lourdes Agapito",
booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",
}