%0 Journal Article %1 T_2015 %A Jaba, Paul. T. %A S., Ms. Jackulin Asha G. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Action action factorization matrix nonnegative recognition representation sparse unit %N 4 %P 1831--1835 %R 10.17762/ijritcc2321-8169.150420 %T Action Recognition using HighLevel Action Units %U http://dx.doi.org/10.17762/ijritcc2321-8169.150420 %V 3 %X Vision-based human recognition is the process of naming image sequences with action labels. In this project, a model is developed for human activity detection using high-level action units to represent human activity. Training phase learns the model for action units and action classifiers. Testing phase uses the learned model for action prediction.Three components are used to classify activities such as New spatial- temporal descriptor, Statistics of the context-aware descriptors, Suppress noise in the action units. Representing human activities by a set of intermediary concepts called action units which are automatically learned from the training data. At low-level, we have existing a locally weighted word context descriptor to progress the traditional interest-point-based representation. The proposed descriptor incorporates the neighborhood details effectively. At high-level, we have introduced the GNMF-based action units to bridge the semantic gap in activity representation. Moreover, we have proposed a new joint l2,1-norm based sparse model for action unit selection in a discriminative manner. Broad experiments have been passed out to authorize our claims and have confirmed our intuition that the action unit based representation is dangerous for modeling difficult activities from videos.

@article{T_2015, abstract = {Vision-based human recognition is the process of naming image sequences with action labels. In this project, a model is developed for human activity detection using high-level action units to represent human activity. Training phase learns the model for action units and action classifiers. Testing phase uses the learned model for action prediction.Three components are used to classify activities such as New spatial- temporal descriptor, Statistics of the context-aware descriptors, Suppress noise in the action units. Representing human activities by a set of intermediary concepts called action units which are automatically learned from the training data. At low-level, we have existing a locally weighted word context descriptor to progress the traditional interest-point-based representation. The proposed descriptor incorporates the neighborhood details effectively. At high-level, we have introduced the GNMF-based action units to bridge the semantic gap in activity representation. Moreover, we have proposed a new joint l2,1-norm based sparse model for action unit selection in a discriminative manner. Broad experiments have been passed out to authorize our claims and have confirmed our intuition that the action unit based representation is dangerous for modeling difficult activities from videos.}, added-at = {2015-08-18T08:55:15.000+0200}, author = {Jaba, Paul. T. and S., Ms. Jackulin Asha G.}, biburl = {https://www.bibsonomy.org/bibtex/2bc0a6604e6879a794b154e1467419795/ijritcc}, doi = {10.17762/ijritcc2321-8169.150420}, interhash = {66fa678c01c6c85a2e674b8e9cdb59ae}, intrahash = {bc0a6604e6879a794b154e1467419795}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Action action factorization matrix nonnegative recognition representation sparse unit}, month = {april}, number = 4, pages = {1831--1835}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-18T08:55:15.000+0200}, title = {Action Recognition using {HighLevel} Action Units}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150420}, volume = 3, year = 2015 }