RECOGNITION OF BASIC HUMAN ACTIONS USING DEPTH INFORMATION

2014 ◽  
Vol 28 (02) ◽  
pp. 1450004 ◽  
Author(s):  
ALI SEYDI KEÇELI ◽  
AHMET BURAK CAN
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Depth Information ◽  
Human Actions ◽  
Depth Sensors ◽  
Vector Machines ◽  
3D Environments ◽  
Game Console

Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information can provide robust features about 3D environments and increase accuracy of action recognition in short ranges. This paper presents an approach to recognize basic human actions using depth information obtained from the Kinect sensor. To recognize actions, features extracted from angle and displacement information of joints are used. Actions are classified using support vector machines and random forest (RF) algorithm. The model is tested on HUN-3D, MSRC-12, and MSR Action 3D datasets with various testing approaches and obtained promising results especially with the RF algorithm. The proposed approach produces robust results independent from the dataset with simple and computationally cheap features.

scholarly journals Mining Key Skeleton Poses with Latent SVM for Action Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoqiang Li ◽  
Yi Zhang ◽  
Dong Liao
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Research Field ◽  
Superior Performance ◽  
Support Vector ◽  
Depth Sensors ◽  
Latent Svm ◽  
Active Research ◽  
Simple Action

Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods.

scholarly journals FRAME SAMPLING ASSISTED DEPTH MOTION MAPS FOR DEPTH BASED HUMAN ACTION RECOGNITION

2021 ◽  
Vol 9 (1) ◽  
pp. 240-246
Author(s):  
Sivanagi Reddy Kalli, K. Mohanram, S. Jagadeesh
Keyword(s):  
Action Recognition ◽  
Image Data ◽  
Human Action Recognition ◽  
Human Action ◽  
Superior Performance ◽  
Depth Image ◽  
Support Vector ◽  
Depth Sensors ◽  
Depth Images ◽  
Key Frames

The discovery of depth sensors has brought new opportunities in the Human Action Research by providing depth image data. Compared to the conventional RGB image data, the depth image data has additional benefits like color, illumination invariant, and provides clues about the shape of body. Inspired with these benefits, we present a new Human Action Recognition model from depth images. For a given action video, the consideration of an entire frames constitutes less detailed information about the shape and movements of body. Hence we have proposed a new method called Frame Sampling to reduce the frame count and chooses only key frames. After key frames extraction, they are processed through Depth Motion Map for action representation followed by Support Vector Machine for classification. The developed model is evaluated on a standard public dataset captured by depth cameras. The experimental results demonstrate the superior performance compared with state-of-art methods

scholarly journals Human action recognition using support vector machines and 3D convolutional neural networks

2017 ◽  
Vol 3 (1) ◽  
pp. 47 ◽  
Author(s):  
Majd Latah
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Recognition Task ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Vector Machines

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.

scholarly journals A novel feature fusion based Human Action Recognition in 2D Videos

2018 ◽  
Vol 7 (2.20) ◽  
pp. 207 ◽  
Author(s):  
K Rajendra Prasad ◽  
P Srinivasa Rao
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Human Actions

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.  

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