Hierarchical spatio-temporal context modeling for action recognition

Sun, Ju; Wu, Xiao; Yan, Shuicheng; Cheong, Loong-Fah; Chua, Tat-Seng; Li, Jintao

doi:10.1109/cvpr.2009.5206721

Cited by 136 publications

(8 citation statements)

References 18 publications

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“…In order to capture temporal information of actions, Wang et al [18] proposed a feature trajectory method, which tracks points of interest and describes changes in the motion characteristics of the points of interest. Sun et al [19] searched for tracks by matching corresponding feature points on continuous video images, and used average descriptors to generate track representations. L. Zelnik-Manor in [20] designed a simple statistical distance measure between video sequences which captures the similarities in their behavioral content.…”

Section: Feature-based Methodsmentioning

confidence: 99%

A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

Zhu

Wang

et al. 2023

Applied Sciences

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Human action recognition algorithms have garnered significant research interest due to their vast potential for applications. Existing human behavior recognition algorithms primarily focus on recognizing general behaviors using a large number of datasets. However, in industrial applications, there are typically constraints such as limited sample sizes and high accuracy requirements, necessitating algorithmic improvements. This article proposes a graph convolution neural network model that combines prior knowledge supervision and attention mechanisms, designed to fulfill the specific action recognition requirements for workers installing solar panels. The model extracts prior knowledge from training data, improving the training effectiveness of action recognition models and enhancing the recognition reliability of special actions. The experimental results demonstrate that the method proposed in this paper surpasses traditional models in terms of recognizing solar panel installation actions accurately. The proposed method satisfies the need for highly accurate recognition of designated person behavior in industrial applications, showing promising application prospects.

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Section: Feature-based Methodsmentioning

confidence: 99%

A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

Zhu

Wang

et al. 2023

Applied Sciences

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“…There are three different types of input modalities that represent the actions performed: the RGB (color images), depth maps and skeleton information. The space-time volumes, spatiotemporal features and trajectories have been utilized for action recognition through the color images in the earlier methods proposed by Schuldt et al (2004), Dollar et al (2005), Sun et al (2009).…”

Section: Computational Models Of Action Recognitionmentioning

confidence: 99%

Online recognition of unsegmented actions with hierarchical SOM architecture

Gharaee

2020

Cogn Process

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Automatic recognition of an online series of unsegmented actions requires a method for segmentation that determines when an action starts and when it ends. In this paper, a novel approach for recognizing unsegmented actions in online test experiments is proposed. The method uses self-organizing neural networks to build a three-layer cognitive architecture. The unique features of an action sequence are represented as a series of elicited key activations by the first-layer self-organizing map. An average length of a key activation vector is calculated for all action sequences in a training set and adjusted in learning trials to generate input patterns to the second-layer self-organizing map. The pattern vectors are clustered in the second layer, and the clusters are then labeled by an action identity in the third layer neural network. The experiment results show that although the performance drops slightly in online experiments compared to the offline tests, the ability of the proposed architecture to deal with the unsegmented action sequences as well as the online performance makes the system more plausible and practical in real-case scenarios.

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“…A hierarchical structure to model the spatial-temporal context information using SIFT has been proposed in [19] to compute trajectories by matching SIFT descriptors between two successive frames. Their model consists of point-level context, intra-trajectory context, and inter-trajectory context.…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition via Depth Maps Body Parts of Action

2018

KSII TIIS

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Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.

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Hierarchical spatio-temporal context modeling for action recognition

Cited by 136 publications

References 18 publications

A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

A Prior Knowledge-Guided Graph Convolutional Neural Network for Human Action Recognition in Solar Panel Installation Process

Online recognition of unsegmented actions with hierarchical SOM architecture

Human Action Recognition via Depth Maps Body Parts of Action

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