Application-Driven AI Paradigm for Human Action Recognition

• URL: http://arxiv.org/abs/2209.15271v1
• Date: Fri, 30 Sep 2022 07:22:01 GMT
• Title: Application-Driven AI Paradigm for Human Action Recognition
• Authors: Zezhou Chen, Yajie Cui, Kaikai Zhao, Zhaoxiang Liu and Shiguo Lian
• Abstract summary: This paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Some experimental results show that the unified framework is effective for various application scenarios.
• Score: 2.0342996661888995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human action recognition in computer vision has been widely studied in recent years. However, most algorithms consider only certain action specially with even high computational cost. That is not suitable for practical applications with multiple actions to be identified with low computational cost. To meet various application scenarios, this paper presents a unified human action recognition framework composed of two modules, i.e., multi-form human detection and corresponding action classification. Among them, an open-source dataset is constructed to train a multi-form human detection model that distinguishes a human being's whole body, upper body or part body, and the followed action classification model is adopted to recognize such action as falling, sleeping or on-duty, etc. Some experimental results show that the unified framework is effective for various application scenarios. It is expected to be a new application-driven AI paradigm for human action recognition.

Related papers

• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning [0.0]
  This paper proposes a transfer learning methodology for activity recognition of construction workers. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner.
  arXiv  Detail & Related papers  (2023-08-28T19:03:46Z)
• Actor-agnostic Multi-label Action Recognition with Multi-modal Query [42.38571663534819]
  Existing action recognition methods are typically actor-specific. This requires actor-specific pose estimation (e.g., humans vs. animals) We propose a new approach called 'actor-agnostic multi-modal multi-label action recognition'
  arXiv  Detail & Related papers  (2023-07-20T10:53:12Z)
• DOAD: Decoupled One Stage Action Detection Network [77.14883592642782]
  Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. We present a decoupled one-stage network dubbed DOAD, to improve the efficiency for-temporal action detection.
  arXiv  Detail & Related papers  (2023-04-01T08:06:43Z)
• iCub! Do you recognize what I am doing?: multimodal human action recognition on multisensory-enabled iCub robot [0.0]
  We show that the proposed multimodal ensemble learning leverages complementary characteristics of three color cameras and one depth sensor. The results indicate that the proposed models can be deployed on the iCub robot that requires multimodal action recognition.
  arXiv  Detail & Related papers  (2022-12-17T12:40:54Z)
• Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing [65.87931036949458]
  Part-level Action Parsing aims at part state parsing for boosting action recognition in videos. We present a simple yet effective approach, named disentangled action parsing (DAP)
  arXiv  Detail & Related papers  (2021-11-05T02:29:32Z)
• Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [111.87412719773889]
  We propose a joint learning framework for "interacted object localization" and "human action recognition" based on skeleton data. Our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition.
  arXiv  Detail & Related papers  (2021-10-28T10:09:34Z)
• Recent Progress in Appearance-based Action Recognition [73.6405863243707]
  Action recognition is a task to identify various human actions in a video. Recent appearance-based methods have achieved promising progress towards accurate action recognition.
  arXiv  Detail & Related papers  (2020-11-25T10:18:12Z)
• A Grid-based Representation for Human Action Recognition [12.043574473965318]
  Human action recognition (HAR) in videos is a fundamental research topic in computer vision. We propose a novel method for action recognition that encodes efficiently the most discriminative appearance information of an action. Our method is tested on several benchmark datasets demonstrating that our model can accurately recognize human actions.
  arXiv  Detail & Related papers  (2020-10-17T18:25:00Z)
• Action similarity judgment based on kinematic primitives [48.99831733355487]
  We investigate to which extent a computational model based on kinematics can determine action similarity. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features.
  arXiv  Detail & Related papers  (2020-08-30T13:58:47Z)
• Human Action Recognition without Human [16.328530279277057]
  We consider whether a background sequence alone can classify human actions in current large-scale action datasets. An experiment clearly shows the effect of a background sequence for understanding an action label.
  arXiv  Detail & Related papers  (2016-08-29T01:22:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.