Weighted Classification of Machine Learning to Recognize Human Activities

Abstract

This paper presents a new method to recognize human activities based on weighted classification for the features extracted by human body. Towards this end, new features depend on weight taken from image or video used in proposed descriptor. Human pose plays an important role in extracted features; then these features are used as the weight input with classifier. We use machine learning during two steps of training and testing images of standard dataset that can be used during benchmarking the system. Unlike previous methods that need size or length of shapes mainly to represent the cues when machine learning is used to recognize human activities, accurate experimental results coming from appropriate segments of the human body proved the worthiness of proposed method. Twelve activities are used in challenging of availability comparison with dataset to demonstrate our method. The results show that we achieved 87.3% in training set, while in testing set, we achieved 94% in terms of precision.

An Efficient Feature Selection Method for Video-Based Activity Recognition Systems

“…However, the random projection-based conversion matrix is produced without taking the fundamental structure of the original data that commonly leads to comparatively high misrepresentation [23]. Similarly, a new model was developed in [24] for the categorization of human activities. In their approach, they suggested new features from the angles derived from the human body parts in order to extract best features, which relies on weight descriptor considered from activity frames against various poses.…”