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

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.

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A Novel Detection Framework for Detecting Abnormal Human Behavior

Mathematical Problems in Engineering ◽

10.1155/2020/6625695 ◽

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.

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-018-1230-2 ◽

2018 ◽

Vol 2018 (1) ◽

Cited By ~ 6

Author(s):

Xiaochao Dang ◽

Yaning Huang ◽

Zhanjun Hao ◽

Xiong Si

Keyword(s):

Human Behavior ◽

Behavior Detection ◽

Device Free

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Towards Intelligent Human Behavior Detection for Video Surveillance

Advancements in Computer Vision and Image Processing - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5628-2.ch003 ◽

2018 ◽

pp. 34-73 ◽

Cited By ~ 1

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.

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Preliminary Study of Discrimination of Human Vocal Commands in Walrus (Odobenus rosmarus divergens)

International Journal of Comparative Psychology ◽

10.46867/ijcp.2020.33.00.03 ◽

2020 ◽

Vol 33 ◽

Author(s):

Shiho Endo ◽

Naoki Kawaguchi ◽

Yusuke Shimizu ◽

Asuka Imagawa ◽

Tomohiro Suzuki ◽

...

Keyword(s):

Social Context ◽

Acoustic Signals ◽

Body Movements ◽

Wooden Board ◽

Odobenus Rosmarus Divergens ◽

Odobenus Rosmarus ◽

Auditory Sense ◽

The Subject ◽

Preliminary Study ◽

Almost All

Walruses seem to use various acoustic signals in social context. So, the auditory faculty is seems to be important for walruses. Can walruses understand another animals' vocal information using auditory sense? This study tested whether a male walrus could discriminate human vocal words and perform different actions corresponding to each one under various conditions. The subject, a male walrus (Odobenus rosmarus) named Pou, was set on the ground, and the experimenter spoke one of the ten words to the subject under the following conditions; (1) The experimenter stood close to the subject and spoke each vocal stimulus wearing a black cloak and goggles so that the experimenter's eye and body movements would not influence the subject's behavior, (2) A wooden board was placed between the experimenter and the subject so that the subject could not see the experimenter, (3) A wooden board was placed between the experimenter and the subject so that the subject could not to see the experimenter, and the experimenter uttered each vocal stimulus through an audio speaker. Under each condition, when the subject performed the correct action corresponding to the vocal stimulus, he was rewarded with a piece of fish. As a result, the subject responded correctly to almost all the human vocal stimuli in every condition, including when the speaker was not visible. This means that he was indeed responding to the vocal words and not the experimenter's cues. This study demonstrated that walruses can hear and identify human vocal words using their auditory sense and can form correspondence between vocal words and their meanings.

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Dialog Structure Through the Lens of Gender, Gender Environment, and Power

Dialogue & Discourse ◽

10.5087/dad.2017.202 ◽

2017 ◽

Vol 8 (2) ◽

pp. 21-55

Author(s):

Vinodkumar Prabhakaran ◽

Owen Rambow

Keyword(s):

Social Context ◽

Human Behavior ◽

Social Contexts ◽

Social Scientists ◽

Gender Information ◽

The Social ◽

Computer Scientists ◽

Automatic Methods ◽

And Gender ◽

Textual Content

Understanding how the social context of an interaction affects our dialog behavior is of great interest to social scientists who study human behavior, as well as to computer scientists who build automatic methods to infer those social contexts. In this paper, we study the interaction of power, gender, and dialog behavior in organizational interactions. In order to perform this study, we first construct the Gender Identified Enron Corpus of emails, in which we semi-automatically assign the gender of around 23,000 individuals who authored around 97,000 email messages in the Enron corpus. This corpus, which is made freely available, is orders of magnitude larger than previously existing gender identified corpora in the email domain. Next, we use this corpus to perform a largescale data-oriented study of the interplay of gender and manifestations of power. We argue that, in addition to one’s own gender, the “gender environment” of an interaction, i.e., the gender makeup of one’s interlocutors, also affects the way power is manifested in dialog. We focus especially on manifestations of power in the dialog structure — both, in a shallow sense that disregards the textual content of messages (e.g., how often do the participants contribute, how often do they get replies etc.), as well as the structure that is expressed within the textual content (e.g., who issues requests and how are they made, whose requests get responses etc.). We find that both gender and gender environment affect the ways power is manifested in dialog, resulting in patterns that reveal the underlying factors. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions.

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