scholarly journals Action Recognition From Weak Alignment of Body Parts

2014 ◽  
Author(s):  
Minh Hoai ◽  
Lubor Ladicky ◽  
Andrew Zisserman
Keyword(s):  
Action Recognition ◽  
Body Parts ◽  
Weak Alignment

A Hierarchical Model for Action Recognition Based on Body Parts

2018 ◽  
Author(s):  
Zhanpeng Shao ◽  
Youfu Li ◽  
Yao Guo ◽  
Jianyu Yang ◽  
Zhenhua Wang
Keyword(s):  
Action Recognition ◽  
Hierarchical Model ◽  
Body Parts

A Hierarchical Model for Human Action Recognition From Body-Parts

2019 ◽  
Vol 29 (10) ◽  
pp. 2986-3000 ◽  
Author(s):  
Zhanpeng Shao ◽  
Youfu Li ◽  
Yao Guo ◽  
Xiaolong Zhou ◽  
Shengyong Chen
Keyword(s):  
Action Recognition ◽  
Hierarchical Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Body Parts

scholarly journals The Complex Action Recognition via the Correlated Topic Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia ◽  
Zheng-wu Wang
Keyword(s):  
Optical Flow ◽  
Action Recognition ◽  
Topic Model ◽  
Body Part ◽  
Research Area ◽  
Body Parts ◽  
Complex Action ◽  
Motion Feature ◽  
Correlated Topic Model ◽  
Human Complex

Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstructing the missing data of point trajectories. Then, we can extract the key frame based on motion feature from optical flow and the ratios of the width and height are extracted by the human silhouette. Finally, we use the topic model of correlated topic model (CTM) to classify action. Experiments were performed on the KTH, Weizmann, and UIUC action dataset to test and evaluate the proposed method. The compared experiment results showed that the proposed method was more effective than compared methods.

Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Chunyan Xu ◽  
Rong Liu ◽  
Tong Zhang ◽  
Zhen Cui ◽  
Jian Yang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Temporal Dynamics ◽  
Recognition Task ◽  
Human Movement ◽  
Body Part ◽  
Human Action ◽  
Human Motion ◽  
Body Parts ◽  
Dynamic Interactions ◽  
Spatio Temporal

In this work, we propose a dual-stream structured graph convolution network ( DS-SGCN ) to solve the skeleton-based action recognition problem. The spatio-temporal coordinates and appearance contexts of the skeletal joints are jointly integrated into the graph convolution learning process on both the video and skeleton modalities. To effectively represent the skeletal graph of discrete joints, we create a structured graph convolution module specifically designed to encode partitioned body parts along with their dynamic interactions in the spatio-temporal sequence. In more detail, we build a set of structured intra-part graphs, each of which can be adopted to represent a distinctive body part (e.g., left arm, right leg, head). The inter-part graph is then constructed to model the dynamic interactions across different body parts; here each node corresponds to an intra-part graph built above, while an edge between two nodes is used to express these internal relationships of human movement. We implement the graph convolution learning on both intra- and inter-part graphs in order to obtain the inherent characteristics and dynamic interactions, respectively, of human action. After integrating the intra- and inter-levels of spatial context/coordinate cues, a convolution filtering process is conducted on time slices to capture these temporal dynamics of human motion. Finally, we fuse two streams of graph convolution responses in order to predict the category information of human action in an end-to-end fashion. Comprehensive experiments on five single/multi-modal benchmark datasets (including NTU RGB+D 60, NTU RGB+D 120, MSR-Daily 3D, N-UCLA, and HDM05) demonstrate that the proposed DS-SGCN framework achieves encouraging performance on the skeleton-based action recognition task.

Action Recognition from Pose Signature in Static Image

2016 ◽  
Vol 30 (03) ◽  
pp. 1655010 ◽  
Author(s):  
Yinzhong Qian ◽  
Wenbin Chen ◽  
I-fan Shen
Keyword(s):  
Performance Improvement ◽  
Action Recognition ◽  
State Of The Art ◽  
Body Part ◽  
The Body ◽  
Whole Body ◽  
Body Parts ◽  
Static Image ◽  
Image Dataset ◽  
Part Structure

This paper addresses the problem of action recognition from body pose. Detecting body pose in static image faces great challenges because of pose variability. Our method is based on action-specific hierarchical poselet. We use hierarchical body parts each of which is represented by a set of poselets to demonstrate the pose variability of the body part. Pose signature of a body part is represented by a vector of detection responses of all poselets for the part. In order to suppress detection error and ambiguity we explore to use part-based model (PBM) as detection context. We propose a constrained optimization algorithm for detecting all poselets of each part in context of PBM, which recover neglected pose clue by global optimization. We use a PBM with hierarchical part structure, where body parts have varying granularity from whole body steadily decreasing to limb parts. From the structure we get models with different depth to study saliency of different body parts in action recognition. Pose signature of an action image is composed of pose signature of all the body parts in the PBM, which provides rich discriminate information for our task. We evaluate our algorithm on two datasets. Compared with counterpart methods, pose signature has obvious performance improvement on static image dataset. While using the model trained from static image dataset to label detected action person on video dataset, pose signature achieves state-of-the-art performance.

scholarly journals Modeling Motion of Body Parts for Action Recognition

2011 ◽  
Author(s):  
Khai Tran ◽  
Ioannis Kakadiaris ◽  
Shishir Shah
Keyword(s):  
Action Recognition ◽  
Body Parts

scholarly journals Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches

2020 ◽  
Vol 10 (8) ◽  
pp. 2811
Author(s):  
Fang Liu ◽  
Liang Zhao ◽  
Xiaochun Cheng ◽  
Qin Dai ◽  
Xiangbin Shi ◽  
...  
Keyword(s):  
Action Recognition ◽  
Saliency Map ◽  
Manual Annotation ◽  
Body Parts ◽  
Fine Grained ◽  
Object Proposal ◽  
Object Proposals ◽  
Huffman Algorithm ◽  
Motion Saliency ◽  
Interaction Component

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.

scholarly journals Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 11045-11052
Author(s):  
Linjiang Huang ◽  
Yan Huang ◽  
Wanli Ouyang ◽  
Liang Wang
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
The State ◽  
Body Parts ◽  
Convolutional Network ◽  
Joint Level ◽  
Convolutional Networks ◽  
Level Information ◽  
Benchmark Datasets ◽  
High Level

Recently, graph convolutional networks have achieved remarkable performance for skeleton-based action recognition. In this work, we identify a problem posed by the GCNs for skeleton-based action recognition, namely part-level action modeling. To address this problem, a novel Part-Level Graph Convolutional Network (PL-GCN) is proposed to capture part-level information of skeletons. Different from previous methods, the partition of body parts is learnable rather than manually defined. We propose two part-level blocks, namely Part Relation block (PR block) and Part Attention block (PA block), which are achieved by two differentiable operations, namely graph pooling operation and graph unpooling operation. The PR block aims at learning high-level relations between body parts while the PA block aims at highlighting the important body parts in the action. Integrating the original GCN with the two blocks, the PL-GCN can learn both part-level and joint-level information of the action. Extensive experiments on two benchmark datasets show the state-of-the-art performance on skeleton-based action recognition and demonstrate the effectiveness of the proposed method.

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