2 research outputs found
3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images
In this paper, we present an approach for identification of actions within
depth action videos. First, we process the video to get motion history images
(MHIs) and static history images (SHIs) corresponding to an action video based
on the use of 3D Motion Trail Model (3DMTM). We then characterize the action
video by extracting the Gradient Local Auto-Correlations (GLAC) features from
the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs
and GLAC features from SHIs are concatenated to obtain a representation vector
for action. Finally, we perform the classification on all the action samples by
using the l2-regularized Collaborative Representation Classifier (l2-CRC) to
recognize different human actions in an effective way. We perform evaluation of
the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD.
Through experimental results, we observe that the proposed method performs
superior to other approaches.Comment: Multimed Tools Appl (2019
Human Activity Recognition using Multi-Head CNN followed by LSTM
This study presents a novel method to recognize human physical activities
using CNN followed by LSTM. Achieving high accuracy by traditional machine
learning algorithms, (such as SVM, KNN and random forest method) is a
challenging task because the data acquired from the wearable sensors like
accelerometer and gyroscope is a time-series data. So, to achieve high
accuracy, we propose a multi-head CNN model comprising of three CNNs to extract
features for the data acquired from different sensors and all three CNNs are
then merged, which are followed by an LSTM layer and a dense layer. The
configuration of all three CNNs is kept the same so that the same number of
features are obtained for every input to CNN. By using the proposed method, we
achieve state-of-the-art accuracy, which is comparable to traditional machine
learning algorithms and other deep neural network algorithms.Comment: IEEE ICET 201