Space-time Templates based Features
for Patient
Activity Recognition
Muhammad Adeel Abbas1,
Fiza Murtaza2,3, Muhammad Obaid Ullah1,
and Muhammad Haroon Yousaf2
1University
of Engineering and Technology, Department of Electrical Engineering, Pakistan
2University
of Engineering and Technology, Department of Computer Engineering, Pakistan
Abstract: Human activity recognition has been the popular area
of research among the computer vision researchers. The proposed work is focused
on patient activity recognition in hospital room environment. We have
investigated the optimum supportive features for the patient activity
recognition problem. Exploiting the strength of space-time template approaches
for activity analysis, Motion-Density Image (MDI) is proposed for patient’s
activities when used supportively with Motion-History Image (MHI). The final
feature vector is created by combining the description of MHI and MDI using
Motion Orientation Histograms (MOH) and then applying Linear Discriminant
Analysis (LDA) for dimensionality reduction. The LDA technique not only reduced
the complexity cost required for classification but also played vital role to
get best recognition results by increasing between-class separation and
decreasing the with-in class variance. To validate the proposed approach, we
recorded a video dataset containing 8 activities of patients performed in
hospital room environment under indoor conditions. We have successfully validated
the results of the proposed approach on our dataset by training the SVM
classifier and achieved 97.9% average recognition accuracy.
Keywords: Human activity recognition, motion templates, patient
monitoring, LDA.
Received September 3, 2019;
accepted July 14, 2020