Results 311 to 320 of about 5,777,447 (338)

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

European Conference on Computer Vision, 2016
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.
Limin Wang, Yuanjun Xiong, Zhe Wang, Y. Qiao, Dahua Lin, Xiaoou Tang, L. Gool   +6 more
semanticscholar   +1 more source

ActionCLIP: Adapting Language-Image Pretrained Models for Video Action Recognition

IEEE Transactions on Neural Networks and Learning Systems, 2023
The canonical approach to video action recognition dictates a neural network model to do a classic and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined categories, limiting their transferability on new datasets ...
Mengmeng Wang, Jiazheng Xing, Jianbiao Mei, Yong Liu, Yunliang Jiang   +4 more
semanticscholar   +1 more source

Skeleton-Based Action Recognition With Shift Graph Convolutional Network

Computer Vision and Pattern Recognition, 2020
Action recognition with skeleton data is attracting more attention in computer vision. Recently, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have obtained remarkable performance.
Ke Cheng, Yifan Zhang, Xiangyu He, Weihan Chen, Jian Cheng, Hanqing Lu   +5 more
semanticscholar   +1 more source

Actionness-Assisted Recognition of Actions

2015 IEEE International Conference on Computer Vision (ICCV), 2015
We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality. These descriptors are mainly trajectory-based, measuring sudden changes, temporal synchrony, and repetitiveness. The actionness map can be used to localize actions in a way that is generic across action and agent types. Furthermore, it also
Ye Luo, Loong-Fah Cheong, An Tran
openaire   +1 more source

Video Action Recognition

2020
Our brain is a superfast action recognition system that’s hard to match. In terms of deep learning, our brain routinely does many things to recognize actions, and it works fast! In this chapter we cover practical methods and tools for video action recognition and classification.
Aytakin Nabisoy, Malekzadeh, Saber
openaire   +2 more sources

Action recognition through discovering distinctive action parts

Journal of the Optical Society of America A, 2015
Recent methods based on midlevel visual concepts have shown promising capabilities in the human action recognition field. Automatically discovering semantic entities such as action parts remains challenging. In this paper, we present a method of automatically discovering distinctive midlevel action parts from video for recognition of human actions.
Feifei, Chen, Nong, Sang, Xiaoqin, Kuang, Haitao, Gan, Changxin, Gao   +4 more
openaire   +2 more sources

AGPN: Action Granularity Pyramid Network for Video Action Recognition

IEEE transactions on circuits and systems for video technology (Print), 2023
Video action recognition is a fundamental task for video understanding. Action recognition in complex spatio-temporal contexts generally requires fusing of different multi-granularity action information.
Yatong Chen, Hongwei Ge, Yuxuan Liu, Xinye Cai, Liang Sun   +4 more
semanticscholar   +1 more source

Action Recognition

2010
Action recognition is one of the most active research fields in computer vision. This chapter first reviews the action recognition methods in literature from two aspects: action representation and recognition strategy. Then, a novel method for classifying human actions from image sequences is investigated.
Qingdi Wei, Xiaoqin Zhang, Weiming Hu
openaire   +1 more source

Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition

European Conference on Computer Vision, 2016
3D action recognition – analysis of human actions based on 3D skeleton data – becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods
Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang   +3 more
semanticscholar   +1 more source

Skeleton-Based Action Recognition With Directed Graph Neural Networks

Computer Vision and Pattern Recognition, 2019
The skeleton data have been widely used for the action recognition tasks since they can robustly accommodate dynamic circumstances and complex backgrounds.
Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu   +3 more
semanticscholar   +1 more source