Case Study
Public Places (Malls, Bus Stops, Streets):
The Problem
Public surveillance typically functions as a passive system, with delayed response to incidents. With increasing crowd volumes, civic bodies and private authorities struggle to detect and prevent events like fights, thefts, and unauthorized gatherings in real time.
The Solution
Our consulting and advisory members engaged the client with preliminary discussions on requirements and needs analysis. Due to the large distribution of data assets in North America and overseas, it became imperative to establish a Data Glossary and Common Data Dictionary (CDD) for each area of operation. There were several Architecture and Design components that encompassed Datawarehouse paradigms, semantic models for reports, dashboard design and concept frameworks for Customer 360 Insights. The Architecture, Data Models and Reports in Business Objects and Cognos were designed as cookie cutters. This allowed for modular development with a reusable framework. The objective was to minimize customizations and individualize country by country.
Our Project Delivery Framework
1. Site Assessment and Use Case Planning
Every public space presents unique security and operational concerns. We begin by assessing the type of location—be it a city bus, train depot, commercial complex, or market—and identifying areas prone to issues like loitering, vandalism, theft, or crowd surges. The assessment helps define use cases like smoking detection, pickpocketing, gender-based crowd analysis, queue monitoring, and fight detection. Stakeholder interviews with public authorities ensure alignment with ground-level realities.
2. Hardware-Agnostic Integration with Field Equipment
Many public locations use outdated or non-uniform CCTV systems. Piloo.ai's lightweight desktop agent enables live video stream relay to the cloud, regardless of the camera brand or age. We integrate with RTSP-compatible streams and use RTMP for low-latency uploads. The app is designed to work with minimal technical input—our support team ensures on-site assistance for onboarding if needed. This allows municipalities and transport authorities to upgrade intelligence without replacing infrastructure.
3. Behavior-Aware AI Detections at Scale
We use specialized YOLO models trained on large public surveillance datasets to detect fights, aggressive body language, lingering individuals, sudden group formations, unattended baggage, or distress scenarios. We also estimate real-time headcount, crowd density, and gender split. Piloo.ai adapts to varying light and weather conditions, allowing accurate performance both inside transport vehicles and outdoor spaces.
4. 30-Day Pilot With Continuous Feedback
The pilot involves deploying Piloo.ai across a fixed set of locations or vehicles. Over the 30-day window, we capture flagged events and provide secure access to the management team for reviewing the footage. Feedback from depot managers, security contractors, or field officers is collected to calibrate the system. Special attention is paid to false positives in crowded areas, ensuring only relevant alerts are delivered.
5. Centralized Command View and Route Analytics
The platform provides a consolidated view of all monitored locations or vehicles. Authorities can track the busiest stops, analyze commuter demographics, view incident clusters, or retrieve videos based on textual or visual queries using our Ask My CCTV™ feature. This central visibility empowers faster responses, operational planning, and public safety audits.
The Technical Approach
For dynamic environments like buses and depots, Piloo.ai integrates RTSP/RTMP streaming from mobile or fixed cameras. YOLO-based multi-class detection identifies suspicious behaviors like loitering, theft, smoking, or fights. Audio-based cues are also processed to detect elevated noise or aggressive tones. Edge-device compatible clients send video to the cloud, where temporal segmentation and class-based prioritization rank events. Crowd density estimation and gender detection models are deployed for demographic analytics. Results are accessible via a responsive dashboard, and automatic incident reports are generated for each flagged event.
Benefits
Key benefits realized by our client encompass:
- Enhance commuter safety by detecting aggressive behavior or criminal intent in real-time.
- Optimize public space usage through people count and heatmap-based crowd analytics
- Improve surveillance team efficiency with automated alerts and event-based video snippets.
- Empower city planners and civic bodies with demographic insights from footfall patterns.
- Build public trust with visible, responsive security infrastructure using Piloo.ai's reports.
Conclusion
Piloo.ai helps build smarter, safer cities by watching what matters when it matters most.
