Advanced Game Analysis Tool Development

I’m building a computer-vision–driven external tool for PC and console games such as Fortnite, NBA 2K27 and Rainbow Six Siege. The system is designed around screen capture and machine vision rather than direct memory access, with a modular overlay, networking/version checks and configurable game profiles. Core pipeline: PS5 / PC game ↓ Remote Play / Capture ↓ Helios / Unity Video Capture ↓ CV frame ↓ Computer Vision ↓ YOLO / Roboflow ↓ Players / heads / objects detected ↓ Tracking + FOV + telemetry ↓ GUI / ESP / Radar Deliverables: • Source-available CV models, overlay renderer and configurable targeting logic • Binary release with self-update and licensing hooks • Performance/testing report across supported games • Setup and API documentation for future game profiles Target requirements: • Stable high-FPS overlay with very low added latency • Runtime-adjustable FOV, smoothing and target/bone priorities • Detailed logs and performance telemetry • Clean, modular code with no hard-coded paths or secrets 1. What Computer Vision is Computer vision (CV) analyzes images or video and extracts information software can reason about. A frame is just pixels. CV converts them into structured data such as: Player: x, y, width, height, confidence Head: x, y, confidence Capture gives you pixels; CV gives those pixels meaning. 2. What Helios does Helios is the host/runtime around the CV pipeline: video frame → CVPython runtime → GCVWorker → Python logic GCVWorker is not the AI. It is the interface between Helios and the Python CV code. A compatible worker exposes methods such as __init__(width, height) and process(frame). 3. What a CV frame is A frame is one image from the live stream. At 60 FPS, each frame is about 16.67 ms apart; at 120 FPS, about 8.33 ms. The system has separate rates: Capture FPS → Inference FPS → Tracking FPS → GUI FPS They do not have to match. 4. What YOLO does YOLO (You Only Look Once) is an object-detection neural network. It can return classes, confidence scores and bounding boxes, which can be converted into center points, head points and distance from the crosshair. 5. Why a game-specific YOLO model matters A generic model may detect “person,” while a game-specific model can be trained for classes such as “player” and “head.” A Fortnite-specific model can therefore provide more useful detections than a generic model. 6. Head detection There are two approaches: • Direct head detection: the model predicts the head location directly. • Head estimation: if only a player box is available, the program estimates the head near the top of that box. Direct head detection is more precise because it uses an actual model output instead of a rough proxy. 7. Target tracking Detection asks, “Where are the players in this frame?” Tracking asks, “Is this the same player I saw before?” Tracking helps reduce target switching and visual jitter. 8. FOV In this CV system, FOV defines how far from the screen center/crosshair a target can be before it is ignored. Smaller FOV: → fewer candidates → less switching → less processing Larger FOV: → more candidates → more choices → potentially more false selections 9. Confidence YOLO returns a confidence value for each detection. A minimum threshold can reject weak detections, but confidence is not a guarantee of correctness; a model can still be confidently wrong. 10. ESP ESP is the visualization layer for CV detections. YOLO finds the target; the ESP renderer draws boxes, head markers, snaplines or other indicators. 11. Radar Radar is another visualization of the same detections, usually as a simplified 2D view rather than an overlay directly on the game image. 12. Visibility checks A simple 2D detector only proves that target pixels are present. It does not reliably prove that a target is visible through the environment. True occlusion analysis can require depth, segmentation, raycasts or game-engine information. A basic YOLO frame alone should not be treated as a true wall/occlusion check. 13. Roboflow Roboflow can host CV models and multi-step workflows. Conceptually: Frame → Workflow → Processing → Result A workflow may contain a model, filtering, postprocessing and output steps. The actual workflow definition should be used rather than guessing what its outputs are. 14. Roboflow in the live pipeline The intended flow is: Helios frame ↓ Latest frame ↓ Roboflow workflow ↓ Detections ↓ Parse result ↓ Tracking ↓ ESP / Radar / Telemetry Latency matters. If capture runs at 60 FPS but an inference request takes 120 ms, processing every frame synchronously creates a backlog. A better real-time design is “latest frame wins”: keep only the newest frame and discard stale ones. 15. Unity Video Capture UnityCapture is a Windows virtual camera/DirectShow mechanism; it is not Fortnite’s renderer. Conceptually: Video source → UnityCapture → Windows camera device → Helios/OpenCV/other program It is useful only when the game image is actually routed through that capture path. If Helios already provides the frame directly, the direct Helios → CVPython path avoids an unnecessary capture layer. 16. Latest-frame architecture If the game produces 60 FPS but the model can process only 30 FPS, queueing every frame makes detection increasingly stale. Better: New frame arrives → replace old frame → process newest frame That keeps analysis close to the current game state instead of processing frames from hundreds of milliseconds ago. 17. Inference latency Inference latency is the time the AI spends processing one image. At 60 FPS, the frame budget is about 16.67 ms, so lower end-to-end CV time generally allows the system to keep up more easily. 18. End-to-end latency CV latency is only one part of the total chain: Controller input → PS5 → Remote Play → video encoding → network → decode/capture → CV → display Therefore: CV latency ≠ total game latency A CV pipeline cannot reduce Remote Play or network latency to zero. 19. GPU inference On supported NVIDIA hardware, model infere