Hub Station Stall and Obstruction Behavior Anomaly Detection Dataset
#Object Detection #Anomaly Behavior Recognition #Traffic Management #Urban Planning #Public Safety
- Records
- 500 records
- Size
- 1.5G
- Format
- MP4/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
AI Analysis & Value Prop
The current transportation sector faces issues with urban traffic disorder, especially at busy hub stations where temporary obstructions like small stalls and parasols severely impact pedestrian and vehicle passage. Existing surveillance systems are mostly static and cannot recognize and handle these dynamic abnormal behaviors in real-time, facing challenges such as low recognition efficiency and slow response. This dataset aims to provide high-quality video data to help researchers and developers enhance their capabilities to detect traffic anomalies, thereby improving urban traffic management. Data collection uses high-definition camera equipment, covering various time periods and weather conditions to ensure data diversity and representativeness. To ensure data quality, we conducted multiple rounds of annotation, consistency checks, and expert reviews to ensure the accuracy of each video and annotation. Data is stored in MP4 format for easy post-processing and analysis, organized by time and location for quick retrieval and application.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| duration | string | Duration |
| quality | string | Resolution |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
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
FAQ
Frequently Asked Questions
- What is the Hub Station Vendor and Road Occupancy Anomaly Detection Dataset?
- The Hub Station Vendor and Road Occupancy Anomaly Detection Dataset is a video understanding dataset focused on detecting vendor and road occupancy behaviors in transportation hubs to enhance traffic management efficiency.
- How can this dataset be used to improve traffic management efficiency?
- By analyzing videos in this dataset, traffic management systems can more effectively identify and address vendor and road occupancy behaviors, thereby improving traffic flow.
- What is the main modality of this dataset?
- The main modality of this dataset is video.
- Which industry is the Hub Station Vendor and Road Occupancy Anomaly Detection Dataset applicable to?
- This dataset is applicable to the transportation industry.
- Why is it necessary to detect vendor and road occupancy behaviors in transport hubs?
- Detecting vendor and road occupancy behaviors in transport hubs helps maintain traffic order and enhances public safety and traffic flow efficiency.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
High-quality data, ready for your next AI breakthrough.
Cite this Work
@dataset{Mobiusi2025,
title={Hub Station Stall and Obstruction Behavior Anomaly Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/0761d9c5fbbb7d179e3be5180b3af6b4?cate=4},
urldate={2025-09-15},
keywords={Traffic Anomaly Detection, Video Dataset, Urban Traffic Management, Event Detection},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder