Human interaction recognition has been widely studied because it has great scientific importance and many potential practical applications.
This work investigates a three-layer convolutional network which uses the Independent Subspace Analysis (ISA) algorithm to learn hierarchical invariant ...
A novel hierarchical model is proposed to capture the implicit and complex interdependencies between interaction class, the action classes of two persons ...
Aug 16, 2021 · Multi-Vision Sensors based Human Interaction Recognition via Hierarchical Invariant Descriptors and Tree Hashing Graph Kernel. August 2021.
In this paper, we propose H-HAR, by rethinking the HAR tasks from a fresh perspective by delving into their intricate global label relationships.
Oct 31, 2017 · Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward ...
In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM ...
In the study, we proposed a two-stream deep learning-based HIR system to address these challenges and improve the accuracy and reliability of HIR systems.
We present a generic method for extracting view-invariant features from skeleton joints. These view-invariant features are further refined using a stacked, ...
Missing: Interaction | Show results with:Interaction
We propose a hierarchical spatial reasoning network for each skeleton frame, which can effectively capture the body-level structural information between ...