ALGORITHM AVENGERS turns Indian Railways' existing CCTV infrastructure into a real-time AI system for crowd management, crime prevention, and staff work monitoring — no new cameras required, no passenger identity stored.
Indian Railways already runs one of the densest CCTV networks in the country — but footage is watched reactively, after an incident, by a handful of control-room staff who cannot monitor hundreds of feeds at once.
A human operator can meaningfully attend to only a handful of screens at once. Across a large station's camera wall, that means most feeds are effectively unwatched — crowd crushes, chain-snatching, trespassing, and safety violations are caught only after a complaint, if at all.
The cameras, cabling, and NVR infrastructure already exist. What's missing is an intelligence layer — models that watch every feed continuously, flag only what matters, and route it to the right person in seconds. ALGORITHM AVENGERS is built to sit on top of the network Indian Railways already owns.
ALGORITHM AVENGERS processes existing CCTV streams through purpose-built computer vision models, organised around the three mandates in the problem statement.
Real-time density estimation across platforms, FOBs, and concourses, with predictive alerts before a crowd becomes a crush.
Behavioural anomaly detection that flags suspicious activity live, instead of relying on post-incident footage review.
Objective, automated tracking of cleaning rounds, staff presence, and safety-gear compliance across shifts.
ALGORITHM AVENGERS is designed as a layer that plugs into the CCTV and NVR infrastructure Indian Railways already operates — no camera replacement, minimal new wiring.
RTSP streams pulled directly from station NVRs — analog feeds passed through low-cost encoders where needed.
A station-side GPU box runs lightweight detection models (YOLO-class) for low-latency, bandwidth-light processing.
Aggregates events across stations, runs heavier models (tracking, re-ID-free crowd counting, pattern prediction).
Rules engine converts model output into prioritised alerts, routed to RPF, station master, or cleaning supervisor.
Operators see live dashboards; field staff get push alerts with camera snapshot, location, and suggested action.
This is a working preview of the operator dashboard — figures below update on a simulated feed to show how ALGORITHM AVENGERS would look in a real control room.
Every layer chosen for real-world station conditions: variable bandwidth, legacy hardware, and 24×7 uptime requirements.
YOLOv8/RT-DETR for object & person detection, DeepSORT-style tracking, OpenPose for posture and PPE checks.
CSRNet-style crowd-counting models producing per-zone density maps without identifying individuals.
Jetson-class edge units at stations, Kubernetes-orchestrated inference in the cloud, RTSP/ONVIF ingestion.
React-based control-room UI, mobile app for field staff, rules engine for alert prioritisation and routing.
From manual footage review after a complaint to a routed alert within seconds of detection.
Every camera watched at all times — not just the handful an operator can actively focus on.
Cleaning rounds and safety checks logged automatically instead of self-reported.
The problem statement itself flags privacy and ethical risk — ALGORITHM AVENGERS treats that as a design constraint, not an afterthought.
Crowd and behaviour models work on movement, posture, and object detection — not on identifying individual passengers.
Raw footage retention follows existing Railway CCTV policy; only flagged event clips are retained longer, with audit logs.
ALGORITHM AVENGERS flags and prioritises — every enforcement action is still taken by an RPF officer or station authority, not the model.
Every alert carries the triggering clip and confidence score, so decisions can be reviewed and models held accountable.
Deploy on existing CCTV at a single major station (e.g. a metro-city terminus), covering crowd density and unattended-object detection only.
Add work-monitoring and behavioural-anomaly models; integrate alert routing with the RPF control room and station master's office.
Extend to all major stations within a division, with a shared cloud analytics backbone and standardised dashboard.
Progressive rollout across zones, prioritised by footfall and incident history, with continuous model retraining on field data.