Human Activity Recognition via Feature Extraction and Artificial Intelligence Techniques: A Review

Abstract

Context: In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques. Methodology: This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities. Results: The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research. Conclusions: It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.

A State-Based Language for Enhanced Video Surveillance Modeling (SEL)

“…The use of motion primitives according to the activity that is desired to be modeled, using a language based on motion primitives, is an expressive and straightforward way to implement modeling for activity inference, particularly for a public inexperienced in managing surveillance systems. state A=[(3, 3),(4, 3),(4, 4), (5,4), (6,4), (6,3),(5, 3)]; state B=[(7, 4), (8,4), (9,4), (10,4)];…”

“…Lateral road entry seq(A, B, C, D) state C=[(11, 5), (11,4), (12,4), (12,5)]; state D=[(13, 4), (13,5), (14,5), (14,4), (15,4), (15,5)];…”

“…state A=[(6, 0),(7, 0),(8, 0)]; state B=[(6, 1),(7, 1),(8, 1)]; state C=[(6, 2),(7, 2),(8, 2)]; Double traffic lanes con(A, B, C, D, E, F, G, H) state D=[(6, 4), (7,4), (8,4)]; state E=[(6, 5), (7,5), (8,5)]; state F=[(6, 6), (7,6), (8,6)]; state G=[(6, 7), (7,7), (8,7)]; state H=[(6, 8), (7,8), (8,8)];…”

“…state A=[(5, 4),(6, 4), (7,4)]; state B=[(8, 4), (9,4), (10,4), (11,4), (12,4)]; state C=[(13, 4), (14,4), (15,4)]; Crossing detection par(A, B, C, D)…”

“…The amount of data generated by the various monitored scenarios is massive, but only some are relevant. Therefore, identifying the critical information in the scenario, detection, and interpretation allow for association with events of interest [4,5].…”