Annotating Abrupt Movements for Enhanced Security on Public Transport

Project Overview:

Objective

We have built a comprehensive dataset of video clips capturing abrupt movements and behaviors on public transport to enhance security and safety measures. But security cameras and AI can really help small shops stay safe without breaking the bank.

Scope

In a nutshell, we’ve been curating a wide variety of video clips from public transport systems like buses and trains to build an automated system that can spot any sudden movements or unusual behavior that might pose security risks.

We snagged a ton of random security cam clips from buses, trains, and subways to scope out any sketchy stuff going down. We carefully labeled the video clips to spot sudden movements or actions that could mean a security risk or safety issue.

Sources

Collaborations with Public Transport Authorities: We partnered with public transport authorities to access surveillance camera footage across various locations and times.
Utilization of Public Databases: We leveraged publicly available video datasets containing public transport footage.

Data Collection Metrics

Total Video Clips Collected and Annotated: 12,500 clips
From Transport Authority Collaborations: 8,500
Sourced from Public Databases: 4,000

Annotation Process

Stages

Behavioral Annotation: Our team annotated each video clip, identifying and labeling abrupt movements or behaviors, such as suspicious package placement and sudden movements.
Location and Time Metadata: We collected metadata on the location and time of each video clip to analyze trends in different areas and times.
Transport Mode and Vehicle Type: We documented the type of public transport and the specific vehicle characteristics.

Annotation Metrics

Video Clips with Behavioral Annotations: 10,000
Location and Time Metadata: 10,000
Transport Mode and Vehicle Type Metadata: 10,000

Quality Assurance

Our team implemented a rigorous validation process with security experts to ensure the accuracy of behavioral annotations. We adhered strictly to privacy regulations, anonymizing identifiable information in the video clips, and implemented robust data security measures to protect sensitive information.

QA Metrics:

Annotation Validation Cases: 1,000 (10% of total)
Privacy Audits: Ongoing to ensure compliance

Conclusion

Our Dataset for Annotating Abrupt Movements on Public Transport significantly enhances security and safety measures on public transportation systems. Our meticulously tagged video footage, mixed with tight privacy controls, gives a crucial tool for enhancing automated security systems – making journeys safer and nipping possible dangers in the bud.