scholarly journals Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition

2020 ◽  
Vol 29 ◽  
pp. 3168-3182 ◽  
Author(s):  
Yang Liu ◽  
Zhaoyang Lu ◽  
Jing Li ◽  
Tao Yang ◽  
Chao Yao
Keyword(s):  
Action Recognition ◽  
Video Adaptation ◽  
Deep Image

Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

2017 ◽  
Vol 47 (4) ◽  
pp. 960-973 ◽  
Author(s):  
Jianguang Zhang ◽  
Yahong Han ◽  
Jinhui Tang ◽  
Qinghua Hu ◽  
Jianmin Jiang
Keyword(s):  
Action Recognition ◽  
Video Adaptation

scholarly journals Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

2021 ◽  
Vol 9 (VIII) ◽  
pp. 760-763
Author(s):  
Rohan Munshi
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Camera Motion ◽  
Video Adaptation ◽  
Surveillance Video ◽  
Systematic Classification ◽  
The Public ◽  
Background Clutter ◽  
Public Datasets

Given a sequence of images i.e. video, the task given a sequence of images i.e. video, the task of action recognition is to identify the most same action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has a lot of applications including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human activity recognition is plagued by numerous challenges thanks to reading changes, occlusion, variation in execution rate, camera motion, and background clutter. In this survey, we provide an overview and report of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification can facilitate researchers to spot the acceptable ways on the market to deal with every one of the challenges visaged and their limitations. In addition to this, we also identify the public datasets and the challenges posed by them. From this survey, we have a tendency to draw conclusions relating to however well a challenge has been resolved, and that we determine potential analysis areas that need more work.

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

Surgical Action Recognition with Spatiotemporal Convolutional Neural Networks

2019 ◽  
Author(s):  
Giacomo De Rossi ◽  
◽  
Nicola Piccinelli ◽  
Francesco Setti ◽  
Riccardo Muradore ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition

Effective video feature for action recognition

2011 ◽  
Vol 31 (2) ◽  
pp. 406-409 ◽  
Author(s):  
Ying-jie LI ◽  
Yi-xin YIN ◽  
Fei DENG
Keyword(s):  
Action Recognition ◽  
Video Feature

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

Fusing Dynamic Images and Depth Motion Maps for Action Recognition in Surveillance Systems

2019 ◽  
Vol 09 ◽  
Author(s):  
Rajat Khurana ◽  
Alok Kumar Singh Kushwaha
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Good Choice ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Learning Capability ◽  
Proposed Model ◽  
Care Activity ◽  
Increasing Demand ◽  
Depth Motion Maps

Background & Objective: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

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