TY - GEN
T1 - Evaluation of vision-based human activity recognition in dense trajectory framework
AU - Kataoka, Hirokatsu
AU - Aoki, Yoshimitsu
AU - Iwata, Kenji
AU - Satoh, Yutaka
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grant Number 24300078.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Activity recognition has been an active research topic in computer vision. Recently, the most successful approaches use dense trajectories that extract a large number of trajectories and features on the trajectories into a codeword. In this paper, we evaluate various features in the framework of dense trajectories on several types of datasets. We implement 13 features in total by including five different types of descriptor, namely motion-, shape-, texture- trajectory- and co-occurrence-based feature descriptors. The experimental results show a relationship between feature descriptors and performance rate at each dataset. Different scenes of traffic, surgery, daily living and sports are used to analyze the feature characteristics. Moreover, we test how much the performance rate of concatenated vectors depends on the type, top-ranked in experiment and all 13 feature descriptors on fine-grained datasets. Feature evaluation is beneficial not only in the activity recognition problem, but also in other domains in spatio-temporal recognition.
AB - Activity recognition has been an active research topic in computer vision. Recently, the most successful approaches use dense trajectories that extract a large number of trajectories and features on the trajectories into a codeword. In this paper, we evaluate various features in the framework of dense trajectories on several types of datasets. We implement 13 features in total by including five different types of descriptor, namely motion-, shape-, texture- trajectory- and co-occurrence-based feature descriptors. The experimental results show a relationship between feature descriptors and performance rate at each dataset. Different scenes of traffic, surgery, daily living and sports are used to analyze the feature characteristics. Moreover, we test how much the performance rate of concatenated vectors depends on the type, top-ranked in experiment and all 13 feature descriptors on fine-grained datasets. Feature evaluation is beneficial not only in the activity recognition problem, but also in other domains in spatio-temporal recognition.
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U2 - 10.1007/978-3-319-27857-5_57
DO - 10.1007/978-3-319-27857-5_57
M3 - Conference contribution
AN - SCOPUS:84952673860
SN - 9783319278568
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 634
EP - 646
BT - Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings
A2 - Elendt, Mark
A2 - Boyle, Richard
A2 - Ragan, Eric
A2 - Parvin, Bahram
A2 - Feris, Rogerio
A2 - McGraw, Tim
A2 - Pavlidis, Ioannis
A2 - Kopper, Regis
A2 - Bebis, George
A2 - Koracin, Darko
A2 - Ye, Zhao
A2 - Weber, Gunther
PB - Springer Verlag
T2 - 11th International Symposium on Advances in Visual Computing, ISVC 2015
Y2 - 14 December 2015 through 16 December 2015
ER -