Non-intrusive human activity recognition and abnormal behavior detection on elderly people: a review

2019 ◽  
Vol 53 (3) ◽  
pp. 1975-2021 ◽  
Author(s):  
Athanasios Lentzas ◽  
Dimitris Vrakas
Keyword(s):  
Elderly People ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

Towards Intelligent Human Behavior Detection for Video Surveillance

2018 ◽  
pp. 34-73 ◽  
Author(s):  
Swati Nigam ◽  
Rajiv Singh ◽  
A. K. Misra
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Behavior ◽  
Human Activity Recognition ◽  
Human Detection ◽  
Video Sequences ◽  
Human Tracking ◽  
Feature Descriptor ◽  
Behavior Detection ◽  
Human Behavior Analysis

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

scholarly journals Anomalous Human Activity Recognition in Surveillance Videos

2019 ◽  
Vol 8 (2S7) ◽  
pp. 350-355
Keyword(s):  
Real Time ◽  
Video Compression ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Abnormal Behavior ◽  
Compressed Video ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Adaptive Video

This paper is a survey on different approaches for Human Activity recognition which has utmost significance in pervasive computing due to its many applications in real-life. Human-oriented problems such as security can be easily taken care of by detecting abnormal behavior. Accurate human activity recognition in real-time is challenging because human activities are complicated and extremely diverse in nature. The traditional Closed-circuit Television (CCTV) system requires to be monitored all the time by a human being, which is inefficient and costly. Therefore, there is a need for a system which can recognize human activity effectively in real-time. It is time-consuming to determine the activity from a surveillance video, due to its size, hence there is a need to compress the video using adaptive compression approaches. Adaptive video compression is a technique that compresses only those parts of the video in which there is least focus, and the rest is not compressed. The objective of the discussion is to be able to implement an automated anomalous human activity recognition system which uses surveillance video to capture the occurrence of an unusual event and caution the user in real-time. So, the paper has two parts that include adaptive video compression approaches of the surveillance videos and providing that compressed video as the input to detect anomalous human activity

Towards Intelligent Human Behavior Detection for Video Surveillance

2019 ◽  
pp. 884-917 ◽  
Author(s):  
Swati Nigam ◽  
Rajiv Singh ◽  
A. K. Misra
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Behavior ◽  
Human Activity Recognition ◽  
Human Detection ◽  
Video Sequences ◽  
Human Tracking ◽  
Feature Descriptor ◽  
Behavior Detection ◽  
Human Behavior Analysis

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

scholarly journals Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

2020 ◽  
Vol 13 (2) ◽  
pp. 139-165 ◽  
Author(s):  
Lisa Schrader ◽  
Agustín Vargas Toro ◽  
Sebastian Konietzny ◽  
Stefan Rüping ◽  
Barbara Schäpers ◽  
...  
Keyword(s):  
Early Intervention ◽  
Elderly People ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition
Sign in / Sign up

Export Citation Format

Share Document