README.md

metadata

tags:
  - Body Language Recognition
  - Video Behavior Analysis
  - Affective Computing
  - Creative Studio
  - Team Collaboration Analysis
  - Non-verbal Communication Research
license: cc-by-nc-sa-4.0
task_categories:
  - video-classification
language:
  - en
pretty_name: Creative Design Studio Interaction Body Language Recognition Video Dataset
size_categories:
  - 1B<n<10B

Creative Design Studio Interaction Body Language Recognition Video Dataset

In today's creative design industry, understanding the complex non-verbal communication among team members is crucial. However, existing body language recognition methods perform limitedly in dense interactive environments, struggling to accurately interpret subtle body movements and postures. This video dataset aims to tackle the technical challenges of body language analysis in creative discussions, enhancing recognition algorithms' accuracy through high-quality video samples. The dataset is collected using HD camera equipment in real design studio environments, recording natural and unobstructed team interaction scenarios. Quality control includes multiple rounds of stringent annotation and consistency checks, reviewed by experienced behavior analysis experts. The annotation team comprises professionals from the fields of computer vision and behavioral psychology, encompassing 30 members. Preprocessing steps involve denoising images, extracting keyframes, and annotating action sequences, with data ultimately stored in a structured MP4 format for easy researcher retrieval and analysis. The dataset boasts excellent data quality, with action annotation accuracy reaching 95% per frame, significantly surpassing similar datasets in consistency and completeness. It employs innovative multimodal annotation methods and data augmentation techniques to aid in developing more robust behavior analysis models. It addresses the industry issue of inadequate recognition accuracy, significantly enhancing team collaboration efficiency. Comparatively, this dataset features unique scene richness and contextual diversity, with its diversity and scarcity making it highly valuable for research. Also, the data's organization and structure provide high versatility and scalability in different research directions, supporting a wider range of application scenarios.

Technical Specifications

Field	Type	Description
file_name	string	File name
duration	string	Duration
quality	string	Resolution
gesture_type	string	The types of gestures identified in the video, such as waving, pointing, etc.
expression_type	string	The types of facial expressions displayed in the video, such as smiling, frowning, etc.
participant_count	integer	The total number of people participating in the discussion in the video.
dominant_gesture	string	The gesture most frequently used in the video.
interaction_density	float	The frequency of interactive behaviors in the video.
mood_level	string	The group emotional state inferred from body language, such as positive, negative, neutral.
speaking_turns	integer	The total number of turns participants take speaking in the video.
proxemics_type	string	The types of interaction distances between participants, such as intimate, social distances, etc.
body_orientation	string	The orientation of participants' bodies in the video, such as facing or sideways to others.
eye_contact_frequency	float	The frequency of eye contact between participants.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com