Abstract
Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene. Based on these silhouettes, full body features and point based features have been extracted which are further optimized with probability based incremental learning (PBIL) algorithm. Finally, random forest classifier has been used to classify activities into different categories. The n-fold cross-validation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71% over other state-of-the-art methodologies.
Highlights
In the world of artificial intelligence, Activities of Daily Living (ADL) has gained much of research interest for assisted living environments
We proposed a novel mechanism for the Red Green Blue (RGB)-D image-based ADL recognition system
4 Experimental Setting and Results In the experimental section, the n-fold cross-validation scheme has been used to evaluate the performance of proposed model over RGBD-AC benchmark datasets
Summary
In the world of artificial intelligence, Activities of Daily Living (ADL) has gained much of research interest for assisted living environments. The ADL is a medical instrument used to evaluate old persons, those with mental illnesses, and others. It is gaining popularity in the measurement of human body motion [2]. The silhouette of RGB image has been extracted through the background subtraction procedure [20]. The pixels of current frame P[I(t)] have been subtracted from the pixels of background images P[I(t − 1)] at the time t that can be depicted using Eq (1) as: P[T] = P[I(t)] − P[I(t − 1)] (1). Where P[T] is the resultant frame of background subtraction. The resultant human silhouette has been further processed for foreground extraction by specifying a threshold Th as described in Eq (2).
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