Anomalous Human Behavior Detection Using a Network of RGB-D Sensors

2018 ◽  
pp. 3-14
Author(s):  
Nicola Mosca ◽  
Vito Renò ◽  
Roberto Marani ◽  
Massimiliano Nitti ◽  
Fabio Martino ◽  
...  
Keyword(s):  
Human Behavior ◽  
Behavior Detection

scholarly journals A Novel Detection Framework for Detecting Abnormal Human Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chengfei Wu ◽  
Zixuan Cheng
Keyword(s):  
Human Behavior ◽  
Public Safety ◽  
Space Time ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Time Block ◽  
Safety Issues ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
Abnormal Behaviors

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.

scholarly journals PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Xiaochao Dang ◽  
Yaning Huang ◽  
Zhanjun Hao ◽  
Xiong Si
Keyword(s):  
Human Behavior ◽  
Behavior Detection ◽  
Device Free

Cognitive Phone for Sensing Human Behavior

2015 ◽  
pp. 1138-1150 ◽  
Author(s):  
Ling Pei ◽  
Robert Guinness ◽  
Jyrki Kaistinen
Keyword(s):  
Social Context ◽  
Mobile Phone ◽  
Human Behavior ◽  
Location Information ◽  
Future Directions ◽  
Behavior Detection ◽  
Current State ◽  
The Social

A boom of various sensor options gives a mobile phone the capability for sensing the social context and makes a mobile phone an attractive “cognitive” platform, which has great potential to model and cognize human behavior. A review of the history, current state, and future directions of the cognitive phone are outlined in this article. An implementation example of a cognitive phone is presented, and a Location-Motion-Context (LoMoCo) model is introduced, to combine personal location information and motion states to infer a corresponding context. Future possibilities of cognitive phones in behavior detection and change are outlined.

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 Preliminary Study of Brain-Inspired Model for Multimodal Human Behavior Detection in Social Context

2021 ◽  
pp. 177-190
Author(s):  
Alessandra Sorrentino ◽  
Laura Fiorini ◽  
Gianmaria Mancioppi ◽  
Olivia Nocentini ◽  
Filippo Cavallo
Keyword(s):  
Social Context ◽  
Human Behavior ◽  
Behavior Detection ◽  
Preliminary Study

Abnormal Human Behavior Detection in Video Surveillance of Conference Halls

2019 ◽  
Vol 14 (2) ◽  
pp. 34
Author(s):  
Nour Charara ◽  
Omar Abou Khaled ◽  
Elena Mugellini
Keyword(s):  
Video Surveillance ◽  
Human Behavior ◽  
Behavior Detection

scholarly journals Anomalous human behavior detection: an adaptive approach

2013 ◽  
Author(s):  
Coen van Leeuwen ◽  
Arvid Halma ◽  
Klamer Schutte
Keyword(s):  
Human Behavior ◽  
Adaptive Approach ◽  
Behavior Detection

scholarly journals Abnormal Human Behavior Detection in Videos: A Review

2021 ◽  
Vol 50 (3) ◽  
pp. 522-545
Author(s):  
Huiyu Mu ◽  
Ruizhi Sun ◽  
Gang Yuan ◽  
Yun Wang
Keyword(s):  
Video Surveillance ◽  
Human Behavior ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Behavior Detection ◽  
Event Modeling ◽  
Semantic Inference ◽  
Abnormal Behavior Detection ◽  
Private And Public ◽  
New Perspective

Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

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