Development of an AI-Based Virtual Camera Tracking System for Coarse Alignment of Mobile Free Space Optical Communication (FSOC) Terminals
“ISRO needs a software simulator to practice aiming ultra-thin laser communication beams between moving space vehicles or drones. Instead of building expensive hardware, students must create a virtual world with a moving target, an AI camera that tracks it, and simulated environmental disturbances like atmospheric wind and shaking.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A standalone software application featuring a 2D/3D physics-based simulation environment, a movable virtual pan-tilt camera, an AI-powered optical beacon detection and tracking pipeline, a disturbance injection engine (turbulence, vibration, noise), and a real-time analytics dashboard.
WHO IS IT FOR?
ISRO scientists, aerospace communication engineers, and researchers developing Free Space Optical Communication (FSOC) pointing, acquisition, and tracking (PAT) systems.
WHY DOES IT EXIST?
Laser beams used in space and mobile communication are extremely narrow and sensitive. A tiny angular error causes total signal loss. Testing this on real hardware is prohibitively expensive and risky, hence a robust software simulation platform is required for rapid algorithm development.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Simulation parameters (target velocity, trajectory paths, camera pan-tilt limits), disturbance coefficients (turbulence intensity, platform vibration frequency and amplitude, sensor noise), and configuration profiles.
Rendering the virtual scene, injecting real-time mathematical noise models, running deep learning or computer vision algorithms (e.g., YOLO/CSRT/Kalman Filter) to detect and track the target beacon, and computing pan-tilt adjustments to center the target in the camera FOV.
Real-time interactive GUI visualization of the camera feed, bounding boxes, tracking vectors, control loop signals, and an automated downloadable performance log report (FPS, acquisition time, tracking error, lock retention rate).
CONSTRAINTS
- Must run locally as a standalone executable without requiring external specialized hardware or high-end GPUs.
- Real-time processing capability maintaining stable frame rates (minimum 30 FPS) during heavy disturbance injection.
- High precision sub-pixel target centroid estimation to mirror actual optical tracking constraints.
JURY METRICS
- Target Acquisition Time (time taken by the virtual camera to first locate the beacon)
- Tracking Error (average and maximum angular or pixel offset from the center)
- Lock Retention Rate (percentage of time the target remains within the camera FOV under extreme disturbances)
- System Performance (FPS, CPU/RAM utilization, and automated performance log accuracy)
CORE BOTTLENECK
Designing realistic mathematical models for atmospheric turbulence and high-frequency platform vibrations that genuinely test the robustness of the computer vision tracking loop without making the target completely untrackable.
Why You Should Choose This PS (Pros)
- High relevance to ISRO's cutting-edge optical communication roadmap.
- Clear, self-contained deliverables (standalone app, code, technical report, performance logs) making judging objective.
- Strong visual appeal ('wow factor') with a live graphical simulation of moving laser terminals and camera viewports.
Challenges & Risks to Watch (Cons)
- Requires strong math and physics understanding to realistically simulate optical disturbances and pan-tilt mechanics.
- Balancing simulation frame rates with complex computer vision tracking loops in real time can be tricky.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
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Background Free Space Optical Communication (FSOC) offers unprecedented advantages for next-generation mobile networks, including gigabit-to-terabit data rates, license-free spectrum operation, high immunity to electromagnetic interference, etc. However, deploying FSOC links between mobile platforms (satellites, UAVs presents a severe challenge of pointing, acquisition and tracking (PAT) of highly narrow laser beams. PAT typically happens in two stages: coarse alignment and fine alignment. Coarse alignment is one of the key challenges of PAT, where the transmitting terminal must first locate and maintain the remote terminal within its camera Field-of-View (FOV).
Developing and testing such algorithms on real hardware requires expensive cameras, pan-tilt mechanisms, and optical components & equipment. A software based virtual camera tracking provides an inexpensive and accessible platform for algorithm development and learning.
Description Unlike conventional radio-frequency systems, FSOC relies on a highly directional optical beam. Even a small angular error can prevent successful communication. Before fine pointing mechanism can take over, a coarse alignment stage must:
The participants shall develop this coarse alignment process in software, allowing to develop and validate tracking algorithms without specialized hardware and setup. The following section provides reference parameters and performance criteria to be considered for the software development.
Parameters and Specifications Functional Objective: Develop a software system that autonomously detects, identifies, and continuously tracks a designated moving target within a virtual scene by controlling a virtual camera viewport.
Add 'Parameters and Specifications' table here Expected Solution Participants shall develop an AI-assisted camera tracking system capable of automatically detecting and continuously tracking a moving optical beacon in a simulated video stream while controlling a virtual pan-tilt camera.
The developed software shall be able to:
Software Application A standalone executable application implementing the complete virtual camera tracking system. The application shall provide all the mandatory functions and features as described above.
Source Code Complete source code with proper documentation. The code shall be modular and adequately commented.
Technical Report The technical report (about 10-15 pages) containing problem understanding, system architecture, description of software modules, tracking methods, AI methods (if used), test methodology, performance analysis and future improvements shall be submitted.
User Manual The user manual with the description of installation of software, application operation, parameter configuration, GUI description, etc. shall be submitted. A 3–5 minutes video may also be provided as an optional deliverable for demonstration of the application.
Performance Log The software should be capable of automatically generating a performance report containing simulation duration, FPS, acquisition time, average and maximum tracking error, lock retention rate, processing time, etc.
Evaluation Method and Criteria The solutions developed by participating teams will be evaluated using multi-layered evaluation method. The following table describes stages of evaluation, their weightage and methods.
Add 'Evaluation Method and Criteria' table here