Idea
Real-time multimodal Suspiciosus Activity Recognition platform enhancing public safety through explainable visual surveillance analytics.
Research Paper
Core Innovation
This paper introduces the USE50k dataset and DeepUSEvision framework that integrates enhanced YOLOv12 object detection, dual DCNNs for facial and body-language recognition, and a transformer-based fusion network. This multimodal approach improves suspiciousness estimation accuracy and interpretability over prior single-modality or less adaptive methods.
Why It Matters
Suspiciousness estimation is vital for proactive threat detection in crowded and complex public spaces. This solution improves accuracy and interpretability of risk assessments, enabling faster and more reliable security responses. It scales across diverse environments, supporting safer public venues and critical infrastructure.
Market Size (TAM)
$10–20B TAM for intelligent surveillance systems; $2–5B SAM from public safety and transportation sectors. Driven by rising security concerns and demand for real-time analytics.
Potential Customers & Pain Points
- Public safety agencies – Need accurate real-time threat detection
- Airport and transit security – Require scalable surveillance analytics
- Private security firms – Demand interpretable suspiciousness alerts
- Smart city operators – Seek integrated risk assessment tools.
Business Model
Subscription-based SaaS platform with tiered pricing for public agencies and private security firms; licensing of dataset and models for custom integrations.
Competitive Landscape
- BriefCam
- AnyVision
- Avigilon
- Hikvision
Implementation Challenges
- Integration with existing surveillance infrastructure
- Privacy and regulatory compliance challenges
- Real-time processing constraints in large-scale deployments
Validation Strategy
- Pilot deployments with airport and transit security partners
- Benchmarking against existing surveillance analytics solutions
- User studies to assess interpretability and operational impact
Research Paper Overview
Transformer-Driven Multimodal Fusion for Explainable Suspiciousness Estimation in Visual Surveillance
Summary
This work introduces the USE50k dataset with 65,500 annotated images from diverse public environments and presents DeepUSEvision, a lightweight system combining object detection, facial and body-language analysis, and transformer-based fusion to generate interpretable suspiciousness scores for real-time surveillance.