scholarly journals Localized Trajectories for 2D and 3D Action Recognition

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3503 ◽  
Author(s):  
Konstantinos Papadopoulos ◽  
Girum Demisse ◽  
Enjie Ghorbel ◽  
Michel Antunes ◽  
Djamila Aouada ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Activity ◽  
Human Body ◽  
Image Plane ◽  
Bag Of Words ◽  
Dense Trajectories ◽  
Motion Trajectories ◽  
Body Joints ◽  
Background Motion ◽  
2D And 3D

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.

Human-Body Action Recognition Based on Dense Trajectories and Video Saliency

2020 ◽  
Vol 57 (24) ◽  
pp. 241003
Author(s):  
高德勇 Gao Deyong ◽  
康自兵 Kang Zibing ◽  
王松 Wang Song ◽  
王阳萍 Wang Yangping
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Dense Trajectories ◽  
Video Saliency

scholarly journals GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3499 ◽  
Author(s):  
Wensong Chan ◽  
Zhiqiang Tian ◽  
Yang Wu
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Adjacency Matrix ◽  
Large Scale ◽  
Redundant Information ◽  
Temporal Dimension ◽  
Convolutional Networks ◽  
Effective Operation ◽  
Public Datasets ◽  
Body Joints

Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which contain discriminative information for different actions. In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action. In addition, to filter out the useless and redundant information in the temporal dimension, we propose a simple yet effective operation named gated temporal convolution. These two major novelties ensure the superiority of our proposed method, as demonstrated on three large-scale public datasets: NTU-RGB + D, Kinetics, and NTU-RGB + D 120, and also shown in the detailed ablation studies.

Action Recognition Based on Motion Representing and Reconstructed Phase Spaces Matching of 3D Joint Positions

2013 ◽  
Vol 333-335 ◽  
pp. 675-679
Author(s):  
Yan Tao Zhao ◽  
Bo Zhang ◽  
Xu Guang Zhang ◽  
Xiao Li Li ◽  
Mei Ling Fu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Nonlinear Dynamical Systems ◽  
Human Action Recognition ◽  
Human Action ◽  
Polar Space ◽  
Phase Portraits ◽  
Nonlinear Dynamical ◽  
Human Pose ◽  
Body Joints

This paper presents an efficient and novel framework for human action recognition based on representing the motion of human body-joints and the theory of nonlinear dynamical systems. Our work is motivated by the pictorial structures model and advances in human pose estimation. Intuitively, a collective understanding of human joints movements can lead to a better representation and understanding of any human action through quantization in the polar space. We use time-delay embedding on the time series resulting of the evolution of human body-joints variables along time to reconstruct phase portraits. Moreover, we train SVM models for action recognition by comparing the distances between trajectories of human body-joints variables within the reconstructed phase portraits. The proposed framework is evaluated on MSR-Action3D dataset and results compared against several state-of-the-art methods.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

scholarly journals Action recognition by dense trajectories

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Heng Wang ◽  
Alexander Klaser ◽  
Cordelia Schmid ◽  
Cheng-Lin Liu
Keyword(s):  
Action Recognition ◽  
Dense Trajectories

scholarly journals Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

2019 ◽  
Vol 8 (12) ◽  
pp. 1016-1021
Keyword(s):  
Optical Flow ◽  
Human Activity ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Computational Cost ◽  
The Body ◽  
Support Vector ◽  
3D Network ◽  
Body Joints ◽  
Body Joint

Human Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are compared.

scholarly journals Study on Machine Learning and Deep Learning Methods for Human Action Recognition

2020 ◽  
Author(s):  
Gopika Rajendran ◽  
Ojus Thomas Lee ◽  
Arya Gopi ◽  
Jais jose ◽  
Neha Gautham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Body ◽  
Human Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Robot Interaction ◽  
Learning Approach ◽  
Action Sequence

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

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