A deep survey on supervised learning based human detection and activity classification methods

Human detection and activity recognition is very important research area in the healthcare, video surveillance, pedestrian detection, intelligent vehicle system and home care center. Among the various human activity detection frameworks, the statistical based approach were most intensively studied and used in practice in which pattern recognition was traditionally formulated. More recently, supervised learning based techniques and methods imported from statistical learning theory have deserved increasing attention. Many new supervised learning methods such as transfer learning, multi-instance learning, and the new trends in deep learning techniques have used for the formulation of solutions to the human activity detection. This paper reviews the automatic human detection and their activity recognition in the video sequences and static images. We explain several problems of human detection and activity recognition in different steps such as processing, segmentation of human features extraction and classification. Moreover, discuss the problems in each step and provide the recent state- of-the-art methods, gaps between recent methods, technical difficulties, applications and their challenges. Several features extraction techniques and corresponding problems for human classification have been discussed in details. Special emphasis have been given on convolution neural network that solves the problem of human segmentation, efficient classification and activity recognition. The objective of this review paper is to summarize and review related of the established and recent methods used in various stages of a human detection and activity classification system and identify research topics and applications that are at the forefront of this exciting and challenging field. Further, the evaluation protocols (i.e. datasets and simulation tools) and possible solution of current limitation have been discussed briefly in this survey.