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SIH Buddyby Ganeev Singh
Dev
All problem statements
SIH26150Proceed with cautionacceptance 2/5

Development of a Multi-Vendor DVR/NVR Forensic Analysis Tool for Standardized Acquisition, Recovery, and Analysis of Surveillance Evidence.

National Technical Research Organisation (NTRO) · Blockchain & Cybersecurity · Software

The spec is precise but the work is per-vendor reverse-engineering requiring physical access to each recorder — a team can realistically support one brand deeply, so only take this if you have a DVR to work from and frame it as a proof-of-concept abstraction, not eight-vendor coverage.

What it actually is

Surveillance recorders from different manufacturers each use their own proprietary storage formats, file systems and timestamps, so investigators need a different tool for every brand and struggle to recover deleted footage or maintain a clean chain of custody. The ask is one vendor-agnostic tool that acquires, recovers and analyses footage across the major DVR and NVR brands with standardised, forensically sound workflows.

What to build

A vendor-agnostic forensic tool that automatically identifies the DVR or NVR model, parses the proprietary file system, creates a forensic disk image, extracts video and metadata, decodes the proprietary video formats, recovers deleted footage, normalises timestamps across cameras, and maintains chain of custody with hashing and audit logging across the major OEMs named — Dahua, CP Plus, Hikvision, Honeywell, TP-Link, Godrej, Uniview and Matrix — with standardised reporting and optional intelligent video analytics on the recovered footage.

Smallest thing that wins the room

Point the tool at a disk image from one supported DVR, have it auto-identify the model, parse the proprietary file system, extract and play recovered footage including a deleted segment, and show the normalised timeline with the chain-of-custody hash log.

How crowded this one gets

A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.

Quiet70–160 teams expectedroughly 1 in 60–139 wins it

Quieter than 83% of the 226 · #40 of 226 by expected field

Few teams are likely to go here. The best odds on the board come from statements like this.

Why: defence, intelligence and space bodies drew small fields.

This is a guess, not a fact

Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.

The scores

The number is the shorthand. The line under it is the reason.

What you will be writing

  • Proprietary file system reverse-engineering
  • DVR disk image parsing
  • Proprietary codec decoding (H.264/H.265 container extraction)
  • Deleted footage carving
  • Timestamp normalisation across cameras
  • Hashing + chain-of-custody audit logging
  • Video forensics
  • Surveillance evidence
  • Reverse engineering

Prior art to read before you start

multi-vendor DVR forensic acquisition · proprietary format reverse-engineering · deleted surveillance footage recovery

Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.