From Roadside Video to Automated Traffic Enforcement Workflows
Mountain Lamp Technologies implemented its TruSight AI-powered traffic surveillance platform across critical locations on the Kallakurichi–Salem highway corridor.
The deployment consists of 10 traffic surveillance cameras positioned at identified critical locations, with the primary objective of detecting defined traffic violations, generating supporting evidence and integrating violation events with the e-Challan system.
The project demonstrates how AI-powered video analytics can create a connected workflow from:
Violation Detection → Evidence → e-Challan
Project at a Glance
A focused highway deployment designed to detect violations, identify vehicles and feed structured events into an e-Challan workflow.
The Challenge: Improving Traffic Enforcement Across a Highway Corridor
Highway environments present a different challenge from city intersections. Vehicles travel at higher speeds, traffic conditions can change rapidly, and violations may occur across long stretches of road.
Conventional CCTV provides valuable evidence, but identifying every violation manually can be difficult. The objective was not simply to record highway traffic—it was to create a system that could help identify violations and move them into an enforcement workflow.
The Objective: Detect. Document. Enforce.
The Kallakurichi–Salem highway deployment was designed around four key objectives—creating an AI-enabled traffic violation detection system with a clear path into enforcement.
DETECT → IDENTIFY → DOCUMENT → e-CHALLAN
The Solution: TruSight AI Traffic Enforcement Platform
Mountain Lamp Technologies deployed TruSight as the intelligent video analytics and traffic enforcement layer—connecting cameras at critical highway locations to AI analytics and a centralized monitoring environment.
10-Camera Deployment at Critical Locations
Rather than attempting indiscriminate coverage of the entire highway, the deployment focuses surveillance on identified locations where monitoring and enforcement requirements are particularly relevant.
This approach concentrates AI analytics and enforcement workflows where they can provide operational value—creating a focused traffic enforcement network rather than simply a large collection of cameras.
AI-Based Traffic Violation Detection
TruSight applies computer vision and AI analytics to identify configured traffic violations. A violation is not merely detected visually—it becomes a structured digital event.
ANPR, Vehicle Identification & Evidence Capture
For enforcement workflows, identifying the vehicle associated with an event is critical. Where ANPR is configured, TruSight can detect and process registration plates—creating a direct connection between the observed traffic event and the vehicle involved.
Each event can associate supporting evidence such as vehicle image, number plate image, violation frame, date/time, location and camera information so authorized personnel can review before the event proceeds.
e-Challan Integration & API Connectivity
One of the defining characteristics of this deployment is integrating TruSight with the e-Challan system. Instead of operating as an isolated camera analytics stack, the traffic violation event can connect to the downstream enforcement workflow through an API.
The exact challan generation, validation and issuance process remains subject to the authorization, business rules and workflow defined by the concerned authority.
The Complete Enforcement Workflow
The solution can be understood as a digital chain from road to enforcement—AI-assisted, with human-authorized oversight.
Centralized Monitoring & Event-Driven Operations
Although cameras are distributed across highway locations, traffic events come together in a centralized monitoring environment—live feeds, AI events, violations, vehicle information, evidence images, timestamps, location context, system status and historical records.
This shifts the operating model from continuous watching to event-first attention.
- 10 cameras
- 10 video feeds
- Continuous monitoring
- Manual detection
- 10 cameras
- AI analysis
- Violation + vehicle ID
- Evidence → e-Challan path
Before & After
The Kallakurichi–Salem deployment illustrates the shift from manual surveillance to connected enforcement.
- Traffic
- CCTV
- Video recording
- Manual monitoring
- Manual identification
- Manual enforcement process
- Traffic
- AI camera
- AI video analytics
- Violation + vehicle ID
- Evidence + validation
- e-Challan integration
Operational Benefits
Implementation Approach
A highway AI surveillance project needs coordinated planning across locations, cameras, AI use cases and enforcement integration.
Key principle: the right camera position enables the right AI analytics.
Lessons From the Project
Conclusion: From Violation Detection to e-Challan
The Kallakurichi–Salem highway deployment demonstrates how AI-powered video analytics can be integrated into a traffic enforcement workflow.
With 10 cameras positioned across critical locations, TruSight provides an intelligent layer for traffic violation detection, vehicle identification and evidence generation. Integration with e-Challan creates a digital connection between what happens on the road and the enforcement system.
The result is a shift from Camera → Recording to Camera → AI → Violation → Evidence → e-Challan—the foundation of an intelligent, connected traffic enforcement ecosystem.
From highway violation detection to city-wide traffic surveillance, Mountain Lamp Technologies can help design and implement an intelligent traffic monitoring architecture that connects cameras, AI analytics, vehicle identification, evidence and enforcement workflows.