SIH26127: City-Wide AI Engine for Multi-Camera ANPR Trajectory Tracking and Urban Traffic — SIH 2026 | Wynit

SIH26127: City-Wide AI Engine for Multi-Camera ANPR Trajectory Tracking and Urban Traffic — SIH 2026 | Wynit

What they'll ask you

13

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Sample video feeds or camera stream formats?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific GIS mapping tools or libraries required?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Hardware specifications for the enterprise-grade platform?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

10 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for high-accuracy anpr and ocr engine. How would you build that?
Decoded from SIH26127 itself: An optical character recognition model that achieves greater than 90% accuracy on license plates under various real-world conditions. The brief asks for it by name.
The brief asks for single plate trajectory tracking. How would you build that?
Decoded from SIH26127 itself: A spatial-temporal tracking system that reconstructs and maps the complete travel trajectory of a specific vehicle plate on a GIS map. The brief asks for it by name.
The brief asks for macro traffic flow analytics. How would you build that?
Decoded from SIH26127 itself: An analytics module that computes traffic density, origin-destination patterns, bottlenecks, and real-time heatmaps from aggregated data. The brief asks for it by name.
The brief asks for city traffic analytics dashboard. How would you build that?
Decoded from SIH26127 itself: A centralized, GIS-integrated web platform displaying heatmaps, average vehicle speeds, route densities, and traffic flow trends. The brief asks for it by name.
The brief asks for real-time alert system. How would you build that?
Decoded from SIH26127 itself: An alert system capable of flagging blacklisted vehicles and suspicious route anomalies in real time. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the evaluators will check if the OCR model achieves greater than 90% accuracy across diverse conditions, if the trajectory reconstruction engine accurately plots paths on a GIS map, and if the platform successfully flags blacklisted vehicles in real time.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (369 words)

• Background Modern urban centers deploy vast networks of CCTV and Automatic Number Plate Recognition(ANPR) cameras to manage traffic, enforce traffic laws, and maintain public security. However,most existing systems process these feeds in isolated silos, performing basic license plate detection without effectively linking data across space and time. This lack of integration prevents city authorities from automatically tracking high-interest vehicles across different sectors and limits their ability to extract macro-level traffic movement trends from the existing camera infrastructure. • Description The objective is to develop a robust, centralized AI software platform that processes multicamera feeds across a city-wide ANPR network to accomplish three core functionalities. First, the platform must feature a High-Accuracy ANPR and OCR Engine, which utilizes an advanced Optical Character Recognition model capable of achieving greater than 90% accuracy across diverse realworld conditions such as varying lighting, poor weather, angled shots, motion blur, and dirty or damaged license plates. Second, it requires a Single Plate Trajectory Tracking module to build a spatial-temporal tracking system capable of reconstructing the complete travel trajectory of any specific vehicle plate across the entire city network. This system will map a vehicle's movement history, timestamps, direction, and route on a GIS map using inputs from geographically distributed ANPR cameras. Third, the system must perform Macro Traffic Flow and Movement Analytics by analyzing aggregated camera data to compute and visualize general city-wide traffic dynamics. This includes measuring traffic density, identifying origin-destination patterns,detecting congestion bottlenecks, and providing real-time heatmaps of city traffic movement. • Expected Solution The expected solution is a scalable, enterprise-grade software platform equipped with four key components. It will feature a High-Precision OCR Module powered by a deep-learning model exceeding 90% recognition accuracy for license plates in multi-lane traffic streams. It will include a Trajectory Reconstruction Engine providing a query-based tracking interface that plots a vehicle's historical path chronologically across the city map with accurate timestamps and camera locations. Furthermore, it will integrate a City Traffic Analytics Dashboard to serve as a centralized, GIS-integrated web platform displaying heatmaps, average vehicle speeds, route densities, and traffic flow trends across all camera nodes. Finally, the platform will incorporate an Alert System capable of flagging blacklisted vehicles and suspicious route anomalies in real time.