scholarly journals A method of depth image based human action recognition

2017 ◽  
Author(s):  
Pei Li ◽  
Wanli Cheng
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Depth Image

scholarly journals Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chao Tang ◽  
Huosheng Hu ◽  
Wenjian Wang ◽  
Wei Li ◽  
Hua Peng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Image Features ◽  
Depth Image ◽  
Good Ability ◽  
Multimodal Features ◽  
Feature Based

The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

scholarly journals Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Shirui Huo ◽  
Tianrui Hu ◽  
Ce Li
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Test Sample ◽  
Human Action ◽  
Superior Performance ◽  
Depth Image ◽  
Collaborative Representation ◽  
Depth Images ◽  
Spatiotemporal Information ◽  
Depth Motion Maps

Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs) and extracting deep convolutional neural network (DCNN) features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC) based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

Multi-View Hierarchical Bidirectional Recurrent Neural Network for Depth Video Sequence Based Action Recognition

2018 ◽  
Vol 32 (10) ◽  
pp. 1850033 ◽  
Author(s):  
Xueping Liu ◽  
Yibo Li ◽  
Qingjun Wang
Keyword(s):  
Action Recognition ◽  
Video Sequence ◽  
Single Layer ◽  
Human Action Recognition ◽  
Research Direction ◽  
Human Action ◽  
Depth Image ◽  
Classification Framework ◽  
Depth Video ◽  
High Computational Efficiency

Human action recognition based on depth video sequence is an important research direction in the field of computer vision. The present study proposed a classification framework based on hierarchical multi-view to resolve depth video sequence-based action recognition. Herein, considering the distinguishing feature of 3D human action space, we project the 3D human action image to three coordinate planes, so that the 3D depth image is converted to three 2D images, and then feed them to three subnets, respectively. With the increase of the number of layers, the representations of subnets are hierarchically fused to be the inputs of next layers. The final representations of the depth video sequence are fed into a single layer perceptron, and the final result is decided by the time accumulated through the output of the perceptron. We compare with other methods on two publicly available datasets, and we also verify the proposed method through the human action database acquired by our Kinect system. Our experimental results demonstrate that our model has high computational efficiency and achieves the performance of state-of-the-art method.

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

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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