Depth Silhouettes Context: A New Robust Feature for Human Tracking and Activity Recognition based on Advanced Hidden Markov Model
Ahmad Jalal, Shaharyar Kamal, Daijin Kim
Journal of Multimedia Processing and Technologies, Vol. 6, No. 3, 2015.
DOI: 
Link: http://www.dline.info/jmpt/fulltext/v6n3/v6n3_1.pdf
Abstract:
In this paper, a depth camera-based novel approach for human activity recognition is presented using robust
depth silhouettes context features and advanced Hidden Markov Models (HMMs). During HAR framework, at first, depth
maps are processed to identify human silhouettes from noisy background by considering frame differentiation constraints of
human body motion and compute depth silhouette area for each activity to track human movements in a scene. From the
depth silhouettes context features, temporal frames information are computed for intensity differentiation measurements,
depth history features are used to store gradient orientation change in overall activity sequence and motion difference
features are extracted for regional motion identification. Then, these features are processed by Principal component analysis
for dimension reduction and kmean clustering for code generation to make better activity representation. Finally, we
proposed a new way to model, train and recognize different activities using advanced HMM. Each activity has been chosen
with the highest likelihood value. Experimental results show superior recognition rate, resulting up to the mean recognition
of 57.69% over the state of the art methods for fifteen daily routine activities using IM-Daily Depth Activity dataset. In
addition, MSRAction3D dataset also showed some promising results.