Pose Detection with YOLOv8 using Wolfram Mathematica

1. Introduction

This project implements a comprehensive pose detection system using YOLOv8 (You Only Look Once Version 8) within Wolfram Mathematica. The system employs models trained on the Microsoft COCO dataset for keypoint estimation, enabling detection of human body joints and poses in images.

The project meets the need for accurate human pose estimation in applications like motion analysis, sports analytics, health monitoring, and human-computer interaction. Leveraging YOLOv8's advanced architecture through Mathematica's neural network framework, the system delivers both detection accuracy and computational efficiency.

Core Features:

• 17-point human keypoint detection (nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles)
• Pre-trained YOLO V8 Pose models from the MS-COCO dataset
• Multiple model sizes for varying performance requirements (N, S, M, L, X)
• Confidence scores for each detected keypoint
• Skeleton visualization and pose analysis
• Heat map generation for keypoint probability visualization

2. Methodology / Approach

The system utilizes YOLOv8 Pose, a state-of-the-art deep learning architecture specifically designed for pose estimation tasks. The model processes images through a single forward pass, detecting human bodies and estimating 17 anatomical keypoints simultaneously.

2.1 System Architecture

The pose detection pipeline consists of:

1. Model Loading: Pre-trained YOLOv8 Pose models from Wolfram Neural Net Repository
2. Image Processing: Input image preprocessing and dimension normalization
3. Inference: Detection of bounding boxes, keypoints, and confidence scores
4. Post-Processing: Non-maximum suppression and coordinate transformation
5. Visualization: Keypoint overlay, skeleton drawing, and heatmap generation

2.2 Implementation Strategy

The implementation leverages Wolfram Mathematica's NetModel framework to access pre-trained YOLOv8 Pose models. Custom evaluation functions handle coordinate transformation, filtering low-confidence detections, and rendering visual outputs. The modular design allows flexible usage for different image processing tasks.

3. Mathematical Framework

3.1 Keypoint Detection

The YOLOv8 Pose model predicts 8,400 bounding boxes, each containing:

• Box coordinates: (x, y, width, height)
• Objectness score: Probability of human presence
• 17 keypoints: Each with (x, y, confidence) values

3.2 Coordinate Transformation

Input images are resized to 640×640 pixels with aspect ratio preservation. Detected coordinates are transformed back to original image dimensions:

max = Max[{width, height}]
scale = max / 640
padX = 640 × (1 - width/max) / 2
padY = 640 × (1 - height/max) / 2

x_original = scale × (x_detected - padX)
y_original = scale × (640 - y_detected - padY)

3.3 Non-Maximum Suppression

Overlapping detections are filtered using NMS with configurable overlap threshold (default: 0.5) to retain only the most confident predictions.

4. Requirements

4.1 Software Requirements

• Wolfram Mathematica: Version 12.0 or higher
• NeuralNetworks Paclet: Required for model access

4.2 Model Requirements

• Pre-trained YOLOv8 Pose models (automatically downloaded)
• Available model sizes: N (Nano), S (Small), M (Medium), L (Large), X (Extra Large)

5. Installation & Configuration

5.1 Environment Setup

(* Install NeuralNetworks Paclet *)
PacletInstall["NeuralNetworks"]

(* Load the default model *)
net = NetModel["YOLO V8 Pose Trained on MS-COCO Data"]

5.2 Project Structure

Pose-Detection-with-YOLOv8-using-Wolfram-Mathematica
├── Pose-Detection-with-YOLOv8-using-Wolfram-Mathematica.nb
├── README.md
└── LICENSE

5.3 Model Selection

Choose model size based on requirements:

(* Load specific model size *)
netX = NetModel[{"YOLO V8 Pose Trained on MS-COCO Data", "Size" -> "X"}]

Model	Parameters	Speed	Accuracy
Nano (N)	~3M	Fastest	Good
Small (S)	~3.3M	Fast	Better
Medium (M)	~3.3M	Moderate	Better
Large (L)	~3.3M	Slower	Best
Extra (X)	~3.3M	Slowest	Excellent

6. Usage / How to Run

6.1 Basic Pose Detection

(* Load test image *)
testImage = Import["testImage.jpg"];

(* Get predictions *)
predictions = netevaluate[
  NetModel["YOLO V8 Pose Trained on MS-COCO Data"], 
  testImage
];

(* View results *)
Keys[predictions]
(* Output: {"ObjectDetection", "KeypointEstimation", "KeypointConfidence"} *)

6.2 Visualize Keypoints

(* Extract keypoints *)
keypoints = predictions["KeypointEstimation"];

(* Highlight keypoints on image *)
HighlightImage[testImage, keypoints]

Output:

6.3 Visualize Skeleton

(* Define skeleton connections *)
getSkeleton[personKeypoints_] := 
  Line[DeleteMissing[
    Map[personKeypoints[[#]] &, 
      {{1,2}, {1,3}, {2,4}, {3,5}, {1,6}, {1,7}, 
       {6,8}, {8,10}, {7,9}, {9,11}, {6,7}, {6,12}, 
       {7,13}, {12,13}, {12,14}, {14,16}, {13,15}, {15,17}}
    ], 1, 2
  ]];

(* Draw pose with skeleton *)
HighlightImage[testImage,
  AssociationThread[Range[Length[#]] -> #] & /@ {
    keypoints, 
    Map[getSkeleton, keypoints], 
    predictions["ObjectDetection"][[;;, 1]]
  },
  ImageLabels -> None
]

Output - Keypoints Grouped by Person:

Output - Keypoints Grouped by Type:

Output - Complete Pose with Skeleton:

6.4 Generate Heatmap

(* Create probability heatmap *)
imgSize = 640;
{w, h} = ImageDimensions[testImage];
max = Max[{w, h}];
scale = max/imgSize;
{padx, pady} = imgSize*(1 - {w, h}/max)/2;

res = NetModel["YOLO V8 Pose Trained on MS-COCO Data"][testImage];

heatpoints = Flatten[Apply[
  {{Clip[Floor[scale*(#1 - padx)], {1, w}],
     Clip[Floor[scale*(imgSize - #2 - pady)], {1, h}]} ->
    ColorData["TemperatureMap"][#3]} &,
  res["KeyPoints"], {2}
]];

heatmap = ReplaceImageValue[ConstantImage[1, {w, h}], heatpoints];

(* Overlay on image *)
ImageCompose[testImage, {heatmap, 0.6}]

Output - Probability Heatmap:

Output - Heatmap Overlay:

6.5 Visualize All Bounding Boxes

(* Visualize all 8,400 predicted bounding boxes *)
boxes = Apply[
  (
    x1 = Clip[Floor[scale*(#1 - #3/2 - padx)], {1, w}];
    y1 = Clip[Floor[scale*(imgSize - #2 - #4/2 - pady)], {1, h}];
    x2 = Clip[Floor[scale*(#1 + #3/2 - padx)], {1, w}];
    y2 = Clip[Floor[scale*(imgSize - #2 + #4/2 - pady)], {1, h}];
    Rectangle[{x1, y1}, {x2, y2}]
  ) &, res["Boxes"], 1
];

Graphics[
  MapThread[{EdgeForm[Opacity[Total[#1] + .01]], #2} &, 
    {res["Objectness"], boxes}], 
  BaseStyle -> {FaceForm[], EdgeForm[{Thin, Black}]}
]

Output - All Prediction Boxes:

Output - Boxes Overlaid on Image:

7. Application / Results

7.1 Keypoint Labels

The system detects 17 anatomical keypoints:

1. Nose
2. Left Eye
3. Right Eye
4. Left Ear
5. Right Ear
6. Left Shoulder
7. Right Shoulder
8. Left Elbow
9. Right Elbow
10. Left Wrist
11. Right Wrist
12. Left Hip
13. Right Hip
14. Left Knee
15. Right Knee
16. Left Ankle
17. Right Ankle

7.2 Output Types

Object Detection:

• Bounding boxes around detected persons
• Confidence scores (0-1)

Keypoint Estimation:

• Pixel coordinates {x, y} for each keypoint
• Per-person keypoint arrays

Keypoint Confidence:

• Probability scores (0-1) for each keypoint
• Indicates detection reliability

7.3 Performance Metrics

Metric	Value
Total Parameters	3,348,483
Prediction Boxes	8,400
Keypoints per Person	17
Detection Threshold	0.25 (configurable)
Overlap Threshold	0.5 (configurable)

8. Tech Stack

8.1 Core Technologies

• Platform: Wolfram Mathematica 12.0+
• Framework: Wolfram Neural Net Repository
• Model Architecture: YOLOv8 Pose
• Dataset: MS-COCO (300,000+ images)

8.2 Model Components

Component	Layers	Purpose
Backbone	C2f, Conv, SPPF	Feature extraction
Neck	C2f, Conv	Feature pyramid
Head (Detect)	Conv, Bn	Keypoint prediction
Total Layers	278	Full architecture

8.3 Layer Statistics

• ConvolutionLayer: 73
• BatchNormalizationLayer: 63
• ElementwiseLayer: 65
• PoolingLayer: 3
• ResizeLayer: 2
• Other Layers: 52

9. License

This project is open source and available under the Apache License 2.0.

10. References

1. Ultralytics YOLOv8 Pose Estimation Documentation.
2. Microsoft COCO Keypoint Detection Challenge.
3. Wolfram Research Wolfram Neural Net Repository.

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Acknowledgments

This project utilizes the YOLOv8 Pose model trained on the Microsoft COCO dataset, accessed through Wolfram's Neural Net Repository. Special thanks to Ultralytics for developing YOLOv8 and to the COCO dataset consortium for providing comprehensive pose estimation training data.

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Note: This implementation is designed for research and educational purposes. Ensure you have appropriate computing resources (CPU/GPU) for optimal performance with larger model sizes. The default model provides a good balance between speed and accuracy for most applications.