Spatio-temporal attention mechanisms based model for collective activity recognition

Lu, Lihua; Di, Huijun; Lu, Yao; Zhang, Lin; Wang, Shunzhou

doi:10.1016/j.image.2019.02.012

Cited by 22 publications

(33 citation statements)

References 4 publications

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“…Lu et al [91] proposed a two-level attention mechanism for group activity recognition. The first individual-level attention is guided by pose features to control the hidden state at each time step.…”

Section: Attention Modelingmentioning

confidence: 99%

A Comprehensive Review of Group Activity Recognition in Videos

Wang

Jian

et al. 2021

Int. J. Autom. Comput.

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Human group activity recognition (GAR) has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance, social role understanding and sports video analysis. In this paper, we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years. First, we provide a summary and comparison of 11 GAR video datasets in this field. Second, we survey the group activity recognition methods, including those based on handcrafted features and those based on deep learning networks. For better understanding of the pros and cons of these methods, we compare various models from the past to the present. Finally, we outline several challenging issues and possible directions for future research. From this comprehensive literature review, readers can obtain an overview of progress in group activity recognition for future studies.

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Section: Attention Modelingmentioning

confidence: 99%

A Comprehensive Review of Group Activity Recognition in Videos

Wang

Jian

et al. 2021

Int. J. Autom. Comput.

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“…Group/Collective Activity Recognition: Before the advent of powerful deep learning methods, devising effective hand-crafted features for group activity recognition has been extensively studied, which commonly represent the sequence images with a number of predefined descriptors [6,7,8,9,10,11,12,53]. Lan et al [6] proposed a latent variable framework to jointly model the contextual information of group-person and person-person interaction.…”

Section: Related Workmentioning

confidence: 99%

Fusing motion patterns and key visual information for semantic event recognition in basketball videos

Yang

Wang

et al. 2020

Neurocomputing

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“…[1]. In the literature of activity recognition, most of the works build activity model based on the temporal spatio characters [2,3], which faces the miscalculation with the similar activities because of the few features. Another research bottleneck is the poor robustness of the activity model which is difficult to handle the data missing, noisy data, habit changing, and so on [4,5].…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to inferring the Internet of Things for complex activity recognition

Ning

Zhu

et al. 2019

J Wireless Com Network

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With the rapid development and large-scale uptake of the Internet of Things, smart home is evolving from a vision towards a realistically viable solution for assisted living. Activity recognition is one of the fundamental tasks in order to provide accurate and timely assistance and service. As daily living scenarios are full of similar activities, missing data, and noise, inferring complex activities using knowledge-driven reasoning algorithms suffers from several drawbacks, e.g., real-time raw sensor data segmentation, poor generalization, higher computational complexity, and scalability. To address these problems, this paper proposes a hybrid approach to complex daily activity recognition by merging the first-order logic and probability graphic modeling. Specifically, we develop a novel "Markov logic network" combining data-driven multi-feature and simplified rule-based modeling and inference, thus enabling and supporting the applicability and robustness of daily activity recognition. To evaluate the approach and associated methods, we design a testing scenario with a number of similar activity groups, missing data, or disturbance test datasets in a multi-modeling sensor scene. Initial results show our approach outperforms the traditional approach with a better accuracy in the situations of similar activities with missing data and noise disturbance. Experiments are also conducted to compare the Gibbs sampling and MC-SAT sampling algorithms for Markov logic network, and the results show that the Gibbs is better in our experimental settings.

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Spatio-temporal attention mechanisms based model for collective activity recognition

Cited by 22 publications

References 4 publications

A Comprehensive Review of Group Activity Recognition in Videos

A Comprehensive Review of Group Activity Recognition in Videos

Fusing motion patterns and key visual information for semantic event recognition in basketball videos

A hybrid approach to inferring the Internet of Things for complex activity recognition

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