Profile-based Human Action Recognition using Depth Information

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.

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Human Action Recognition Using Motion History Image Based Temporal Segmentation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141655017x ◽

2016 ◽

Vol 30 (06) ◽

pp. 1655017 ◽

Cited By ~ 5

Author(s):

Shou-Jen Lin ◽

Mei-Hsuan Chao ◽

Chao-Yang Lee ◽

Chu-Sing Yang

Keyword(s):

Action Recognition ◽

Recognition Rate ◽

Three Dimensional ◽

Human Action Recognition ◽

Recognition System ◽

Human Action ◽

Depth Information ◽

Temporal Segmentation ◽

3D Data ◽

2D Data

A human action recognition system based on image depth is proposed in this paper. Depth information features are not easily disturbed by noise; and due to this characteristic, the system can quickly extract foreground targets. Moreover, the target data, namely, depth and two-dimensional (2D) data, are projected to three orthogonal planes. In this manner, the action featured in the depth motion along the optical axis can clearly describe the trajectory. Based on the change of motion energy and the angle variations of motion orientations, the temporal segmentation (TS) method automatically segments the complex action into several simple movements. Three-dimensional (3D) data is further applied to acquire the three-viewpoint (3V) motion history trajectory, whereby a target’s motion is described through the motion history images (MHIs) from the 3Vs. The weightings corresponding to the gradients of the MHIs are included for determining the viewpoint that bests describe the target’s motion. In terms of feature extraction, the application of multi-resolution motion history histograms can effectively reduce the computational load and achieve a high recognition rate. Experimental results demonstrate that the proposed method can effectively solve the self-occlusion problem.

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

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

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.

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Deep Learning for Human Action Recognition Survey

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i10.323328 ◽

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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