Detail publikace

On-line human action detection using space-time interest points

Originální název

On-line human action detection using space-time interest points

Anglický název

On-line human action detection using space-time interest points

Jazyk

en

Originální abstrakt

The on-line human action detection is an important task in human-machine interaction and related applications. One of the possible approaches to the detection is exploitation of space-time interest points. Such points are typically identi ed using feature extractor and then they are processed and classifi ed. The classifi cation can be performed using codebooks built based on feature vectors statistics. The individual feature vectors are transformed into bag of words representation using such codebooks and then the code words are classi ed using SVM. The proposed approach improves the training process and extends the known approaches. The training part of the dataset is split into shorter shots with equal duration and these are annotated and classi ed using a SVM classi er. This ensures that the time-local motion is captured by the SVM while the longer time behavior is left on further processing mechanisms, such as, e.g. HMMs. In the proposed approach, the output of the SVM classi er is simply compared to a threshold and the presence of a value above the threshold indicates that the desired human activity occurred. The contribution describes the approach summarizes the achieved results and draws conclusions.

Anglický abstrakt

The on-line human action detection is an important task in human-machine interaction and related applications. One of the possible approaches to the detection is exploitation of space-time interest points. Such points are typically identi ed using feature extractor and then they are processed and classifi ed. The classifi cation can be performed using codebooks built based on feature vectors statistics. The individual feature vectors are transformed into bag of words representation using such codebooks and then the code words are classi ed using SVM. The proposed approach improves the training process and extends the known approaches. The training part of the dataset is split into shorter shots with equal duration and these are annotated and classi ed using a SVM classi er. This ensures that the time-local motion is captured by the SVM while the longer time behavior is left on further processing mechanisms, such as, e.g. HMMs. In the proposed approach, the output of the SVM classi er is simply compared to a threshold and the presence of a value above the threshold indicates that the desired human activity occurred. The contribution describes the approach summarizes the achieved results and draws conclusions.

BibTex


@inproceedings{BUT76466,
  author="Ivo {Řezníček} and Pavel {Zemčík}",
  title="On-line human action detection using space-time interest points",
  annote="The on-line human action detection is an important task in human-machine
interaction and related applications. One of the possible approaches to the
detection is exploitation of space-time interest points. Such points are
typically identied using feature extractor and then they are processed and
classified. The classification can be performed using codebooks built based on
feature vectors statistics. The individual feature vectors are transformed into
bag of words representation using such codebooks and then
the code words are classied using SVM. The proposed approach improves the
training process and extends the known approaches. The training part of the
dataset is split into shorter shots with equal duration and these are annotated
and classied using a SVM classier. This ensures that the time-local motion is
captured by the SVM while the longer time behavior is left on further processing
mechanisms, such as, e.g. HMMs. In the proposed approach, the
output of the SVM classier is simply compared to a threshold and the presence of
a value above the threshold indicates that the desired human activity occurred.
The contribution describes the approach summarizes the achieved results and draws
conclusions.",
  address="Faculty of Mathematics and Physics, Charles University",
  booktitle="Zborník príspevkov prezentovaných na konferencii ITAT, september 2011",
  chapter="76466",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Faculty of Mathematics and Physics, Charles University",
  year="2011",
  month="september",
  pages="39--45",
  publisher="Faculty of Mathematics and Physics, Charles University",
  type="conference paper"
}