• Title, Summary, Keyword: Human activity recognition

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Hierarchical Deep Belief Network for Activity Recognition Using Smartphone Sensor (스마트폰 센서를 이용하여 행동을 인식하기 위한 계층적인 심층 신뢰 신경망)

  • Lee, Hyunjin
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1421-1429
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    • 2017
  • Human activity recognition has been studied using various sensors and algorithms. Human activity recognition can be divided into sensor based and vision based on the method. In this paper, we proposed an activity recognition system using acceleration sensor and gyroscope sensor in smartphone among sensor based methods. We used Deep Belief Network (DBN), which is one of the most popular deep learning methods, to improve an accuracy of human activity recognition. DBN uses the entire input set as a common input. However, because of the characteristics of different time window depending on the type of human activity, the RBMs, which is a component of DBN, are configured hierarchically by combining them from different time windows. As a result of applying to real data, The proposed human activity recognition system showed stable precision.

Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models

  • Uddin, Md. Zia;Kim, Jaehyoun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.6
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    • pp.2767-2780
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    • 2016
  • Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

  • Kamal, Shaharyar;Jalal, Ahmad;Kim, Daijin
    • Journal of Electrical Engineering and Technology
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    • v.11 no.6
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    • pp.1857-1862
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    • 2016
  • Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map

  • Farooq, Adnan;Jalal, Ahmad;Kamal, Shaharyar
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.5
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    • pp.1856-1869
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    • 2015
  • This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

Human Activity Recognition Using Body Joint-Angle Features and Hidden Markov Model

  • Uddin, Md. Zia;Thang, Nguyen Duc;Kim, Jeong-Tai;Kim, Tae-Seong
    • ETRI Journal
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    • v.33 no.4
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    • pp.569-579
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    • 2011
  • This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time-series activity images acquired with a single stereo camera by co-registering a 3D body model to the stereo information. The estimated joint-angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint-angle-based HAR has been compared to that of a conventional binary and depth silhouette-based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches.

Spatio-Temporal Analysis of Trajectory for Pedestrian Activity Recognition

  • Kim, Young-Nam;Park, Jin-Hee;Kim, Moon-Hyun
    • Journal of Electrical Engineering and Technology
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    • v.13 no.2
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    • pp.961-968
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    • 2018
  • Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

A Robust Approach for Human Activity Recognition Using 3-D Body Joint Motion Features with Deep Belief Network

  • Uddin, Md. Zia;Kim, Jaehyoun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.2
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    • pp.1118-1133
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    • 2017
  • Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

Trends in Activity Recognition Using Smartphone Sensors (스마트폰 기반 행동인식 기술 동향)

  • Kim, M.S.;Jeong, C.Y.;Sohn, J.M.;Lim, J.Y.;Chung, S.E.;Jeong, H.T.;Shin, H.C.
    • Electronics and Telecommunications Trends
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    • v.33 no.3
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    • pp.89-99
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    • 2018
  • Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

Real-time Recognition of Daily Human Activities Using A Single Tri-axial Accelerometer

  • Rubaiyeat, Husne Ara;Khan, Adil Mehmood;Kim, Tae-Seong
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.289-292
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    • 2010
  • Recently human activity recognition using accelerometer has become a prominent research area in proactive computing. In this paper, we present a real-time activity recognition system using a single tri-axial accelerometer. Our system recognizes four primary daily human activities: namely walking, going upstairs, going downstairs, and sitting. The system also computes extra information from the recognized activities such as number of steps, energy expenditure, activity duration, etc. Finally, all generated information is stored in a database as daily log.

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Human Activity Pattern Recognition Using Motion Information and Joints of Human Body (인체의 조인트와 움직임 정보를 이용한 인간의 행동패턴 인식)

  • Kwak, Nae-Joung;Song, Teuk-Seob
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.6
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    • pp.1179-1186
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    • 2012
  • In this paper, we propose an algorithm that recognizes human activity patterns using the human body's joints and the information of the joints. The proposed method extracts the object from inputted video, automatically extracts joints using the ratio of the human body, applies block-matching algorithm for each joint and gets the motion information of joints. The proposed method uses the joints to move, the directional vector of motions of joints, and the sign to represent the increase or decrease of x and y coordinates of joints as basic parameters for human recognition of activity. The proposed method was tested for 8 human activities of inputted video from a web camera and had the good result for the ration of recognition of the human activities.