ArticleEntropy (Basel, Switzerland)2020
Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A novel multi-modal rehabilitation monitoring over human motion intention recognition.Frontiers in bioengineering and biotechnology · 2025Article
- Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.Sensors (Basel, Switzerland) · 2025Article
- Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images.PeerJ. Computer science · 2025Article
- Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.Sensors (Basel, Switzerland) · 2024Article
- TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.Scientific reports · 2024Article
- A robust multimodal detection system: physical exercise monitoring in long-term care environments.Frontiers in bioengineering and biotechnology · 2024Article
- Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Intelligent Localization and Deep Human Activity Recognition through IoT Devices.Sensors (Basel, Switzerland) · 2023Article
- Hand gesture recognition via deep data optimization and 3D reconstruction.PeerJ. Computer science · 2023Article
- Postures anomaly tracking and prediction learning model over crowd data analytics.PeerJ. Computer science · 2023Article
- Extraction of entity relations from Chinese medical literature based on multi-scale CRNN.Annals of translational medicine · 2022Article
- Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.Entropy (Basel, Switzerland) · 2021Article
- Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods.Artificial intelligence in medicine · 2021Article
- Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning.PeerJ. Computer science · 2021Article
- Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning.Entropy (Basel, Switzerland) · 2020Article
- Toward intelligent rehabilitation: Multimodal human pose modeling with parametric meshes and graph-based temporal reasoning.Digital healthArticle
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Authors and funding
3 authors.
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Abstract
Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features-i.e., spatio-temporal features, energy-based features, shape based angular and geometric features-and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man-machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.