Learning Person–Person Interaction in Collective Activity Recognition

Chang, Xiaobin; Zheng, Wei; Zhang, Jianguo

doi:10.1109/tip.2015.2409564

Cited by 34 publications

(5 citation statements)

References 36 publications

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“…Vázquez et al, 2015) and in collective intelligence studies (e.g. Chang et al, 2015). The interactions seem to be about decision-making, and this view may be a topic for future research.…”

Section: Discussionmentioning

confidence: 99%

The utility of indigenous knowledge in the pursuit of a UN sustainable development goal

Beckett,

O’Loughlin

2024

Collective Intelligence

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In this paper, indigenous knowledge is viewed as a form of collective intelligence that may inform situational action. We consider knowledge associated with the use of fire by Indigenous Australians as a land management tool that may help moderate the impact of climate change. Scientific study of the foundation of such knowledge has enhanced our understanding of it, and combination with new technology tools can enhance traditional practices. We draw on the concept of a ‘collective intelligence genome’ identified in the literature to examine aspects of indigenous knowledge capture and application, and an enhanced representation of this concept is presented. We observe knowledge application draws on a set of rules, selection from which is dependent on the state of fauna and weather in a particular microcosm. A representation of multiple if-then decision-making is presented as an example of genome component application.

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“…Vázquez et al, 2015) and in collective intelligence studies (e.g. Chang et al, 2015). The interactions seem to be about decision-making, and this view may be a topic for future research.…”

Section: Discussionmentioning

confidence: 99%

The utility of indigenous knowledge in the pursuit of a UN sustainable development goal

Beckett,

O’Loughlin

2024

Collective Intelligence

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“…Wang et al proposed a hierarchical recognition method to infer ongoing activities in a multistage process to better distinguish similar activities and improve overall performance [ 16 ]. In terms of complex behavior recognition, Chang et al considered the interaction between people and the surrounding environment using sensors distributed on people and objects to collect behavioral data, which improves the extraction of data features of complex behaviors [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

Wang¹

2022

Journal of Healthcare Engineering

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With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices’ ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.

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“…HOG and statistical features are extracted to record human actions. In [39], a connection between atomic activities is measured and interaction responses are formulated. A multi-task interaction response (MIR) was computed for each class separately.…”

Section: Comparison With Other Recently Proposed Systemsmentioning

confidence: 99%

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

et al. 2021

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Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.

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Learning Person–Person Interaction in Collective Activity Recognition

Cited by 34 publications

References 36 publications

The utility of indigenous knowledge in the pursuit of a UN sustainable development goal

The utility of indigenous knowledge in the pursuit of a UN sustainable development goal

Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

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