MOTION INFORMATION COMBINATION FOR FAST HUMAN ACTION RECOGNITION

<p><strong>Abstract.</strong> Accumulating the motion information from a video sequence is one of the highly challenging and significant phase in Human Action Recognition. To achieve this, several classical and compact representations are proposed by the research community with proven applicability. In this paper, we propose a compact Depth Motion Map based representation methodology with hastey striding, consisely accumulating the motion information. We extract Undecimated Dual Tree Complex Wavelet Transform features from the proposed DMM, to form an efficient feature descriptor. We designate a Sequential Extreme Learning Machine for classifying the human action secquences on benchmark datasets, MSR Action 3D dataset and DHA Dataset. We empirically prove the feasability of our method under standard protocols, achieving proven results.</p>

Download Full-text

On the use of local motion information for human action recognition via feature selection

2015 4th International Conference on Electrical Engineering (ICEE) ◽

10.1109/intee.2015.7416792 ◽

2015 ◽

Cited By ~ 6

Author(s):

Ammar Ladjailia ◽

Imed Bouchrika ◽

Hayet Farida Merouani ◽

Nouzha Harrati

Keyword(s):

Feature Selection ◽

Action Recognition ◽

Human Action Recognition ◽

Human Action ◽

Motion Information ◽

Local Motion

Download Full-text

Human action recognition based on deep network and feature fusion

Filomat ◽

10.2298/fil2015967w ◽

2020 ◽

Vol 34 (15) ◽

pp. 4967-4974

Author(s):

Dongli Wang ◽

Jun Yang ◽

Yan Zhou ◽

Zhen Zhou

Keyword(s):

Action Recognition ◽

Feature Fusion ◽

Human Action Recognition ◽

Human Action ◽

Feature Representation ◽

Support Vector ◽

Motion Information ◽

Global Motion ◽

Difference Images ◽

Temporal Feature

Feature representation is of vital importance for human action recognition. In recent few years, the application of deep learning in action recognition has become popular. However, for action recognition in videos, the advantage of single convolution feature over traditional methods is not so evident. In this paper, a novel feature representation that combines spatial and temporal feature with global motion information is proposed. Specifically, spatial and temporal feature from RGB images is extracted by convolutional neural network (CNN) and long short-term memory (LSTM) network. On the other hand, global motion information extracted from motion difference images using another separate CNN. Hereby, the motion difference images are binary video frames processed by exclusive or (XOR). Finally, support vector machine (SVM) is adopted as classifier. Experimental results on YouTube Action and UCF-50 show the superiority of the proposed method.

Download Full-text

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.

Download Full-text