Computer Vision Tool for PC and Console Games
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
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 inference can use CUDA and FP16. This can reduce memory use and improve throughput, but the result depends on the GPU and workload.
20. Image size
YOLO input size affects the speed/detail tradeoff:
640×640 → generally faster, less detail
960×960 → generally slower, more detail
The goal is to balance accuracy and latency for the actual hardware.
21. ROI
ROI means Region of Interest. Restricting detection to a relevant area can reduce the amount of image the model must process.
22. Preprocessing
Before inference, the pipeline may:
capture → crop → resize → color-convert → normalize → model
Poor preprocessing can hurt accuracy even when the model itself is good.
23. Postprocessing
After YOLO returns predictions, the program can:
• filter by confidence/class
• calculate head points
• calculate distances
• select targets
• track targets
• apply smoothing/prediction
YOLO is only one component of the full CV system.
24. Controller vs CV
The controller and CV pipelines are separate.
VIDEO SIDE:
Helios → CV → YOLO/Roboflow
INPUT SIDE:
Controller → Remote Play / controller transport → Windows
They can share information for telemetry, diagnostics or training, but they are fundamentally different subsystems.
25. Visual target lock vs automated input
“Target lock” can mean several different things.
Visual lock:
detect → choose → highlight target
Input correction:
detect → calculate delta → change controller/mouse input
Fully automated aiming:
detect → predict → change input → fire
These are different levels of automation.
26. Prediction
Prediction estimates where a moving target may be on the next frame. For example, if the head moves 800 → 810 → 820, a simple estimate might place the next position near 830.
Prediction works best when combined with tracking and smoothing.
27. Smoothing
Smoothing reduces jitter in detected positions.
More smoothing:
→ steadier
→ more lag
Less smoothing:
→ more responsive
→ more jitter
28. Why real-game testing matters
Different environments can change the visual stream through network delay, Remote Play timing, frame pacing, buffering, resolution, HUD state, motion and compression.
Meaningful testing should use the real Remote Play connection, real game mode and real capture path—not only local testing.
29. Important metrics
Useful metrics include:
• Capture FPS
• CV FPS
• CV time
• Frame age
• Dropped frames
• Detection confidence
• Target switching
• Head error X/Y
These reveal much more than a simple “AI ON” indicator.
30. The full system
The pipeline is not one component:
Remote Play
↓
Video transport
↓
Capture
↓
CVPython
↓
Detection
↓
Tracking
↓
Visualization
↓
Controller
A problem anywhere in this chain can look like an AI problem. For example, poor capture can create blur, lower confidence and target jumping even when the model itself is fine.
Your setup can be summarized as four layers:
• Remote Play — PS5 video/controller connection
• Helios / CVPython — live frame acquisition and processing
• CV / YOLO / Roboflow — understanding the frame
• GUI / ESP / Radar — displaying the results
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 inference can use CUDA and FP16. This can reduce memory use and improve throughput, but the result depends on the GPU and workload.
20. Image size
YOLO input size affects the speed/detail tradeoff:
640×640 → generally faster, less detail
960×960 → generally slower, more detail
The goal is to balance accuracy and latency for the actual hardware.
21. ROI
ROI means Region of Interest. Restricting detection to a relevant area can reduce the amount of image the model must process.
22. Preprocessing
Before inference, the pipeline may:
capture → crop → resize → color-convert → normalize → model
Poor preprocessing can hurt accuracy even when the model itself is good.
23. Postprocessing
After YOLO returns predictions, the program can:
• filter by confidence/class
• calculate head points
• calculate distances
• select targets
• track targets
• apply smoothing/prediction
YOLO is only one component of the full CV system.
24. Controller vs CV
The controller and CV pipelines are separate.
VIDEO SIDE:
Helios → CV → YOLO/Roboflow
INPUT SIDE:
Controller → Remote Play / controller transport → Windows
They can share information for telemetry, diagnostics or training, but they are fundamentally different subsystems.
25. Visual target lock vs automated input
“Target lock” can mean several different things.
Visual lock:
detect → choose → highlight target
Input correction:
detect → calculate delta → change controller/mouse input
Fully automated aiming:
detect → predict → change input → fire
These are different levels of automation.
26. Prediction
Prediction estimates where a moving target may be on the next frame. For example, if the head moves 800 → 810 → 820, a simple estimate might place the next position near 830.
Prediction works best when combined with tracking and smoothing.
27. Smoothing
Smoothing reduces jitter in detected positions.
More smoothing:
→ steadier
→ more lag
Less smoothing:
→ more responsive
→ more jitter
28. Why real-game testing matters
Different environments can change the visual stream through network delay, Remote Play timing, frame pacing, buffering, resolution, HUD state, motion and compression.
Meaningful testing should use the real Remote Play connection, real game mode and real capture path—not only local testing.
29. Important metrics
Useful metrics include:
• Capture FPS
• CV FPS
• CV time
• Frame age
• Dropped frames
• Detection confidence
• Target switching
• Head error X/Y
These reveal much more than a simple “AI ON” indicator.
30. The full system
The pipeline is not one component:
Remote Play
↓
Video transport
↓
Capture
↓
CVPython
↓
Detection
↓
Tracking
↓
Visualization
↓
Controller
A problem anywhere in this chain can look like an AI problem. For example, poor capture can create blur, lower confidence and target jumping even when the model itself is fine.
Your setup can be summarized as four layers:
• Remote Play — PS5 video/controller connection
• Helios / CVPython — live frame acquisition and processing
• CV / YOLO / Roboflow — understanding the frame
• GUI / ESP / Radar — displaying the results
Apply on Freelancer →
Project sourced from Freelancer.com. Applications happen directly on the original platform — we never collect your data.