Project Title: Human Pose Estimation Using Deep Learning

Objective:

To detect and track human body keypoints (joints like elbows, knees, wrists) in images or video, enabling the understanding of human posture, movement, and interaction.

Dataset:

COCO (Common Objects in Context): Contains annotations for keypoints of human poses in images.

MPII Human Pose Dataset: Another dataset with labeled images for human poses in various conditions.

Key Steps:

Data Preprocessing:

Image Preprocessing: Resize, normalize, and augment images to ensure robust learning (rotation, scaling, etc.).

Keypoint Labeling: Each human body in the image is labeled with keypoints (e.g., eyes, shoulders, hips).

Model Architecture:

Convolutional Neural Networks (CNNs): Used to extract spatial features from images.

Hourglass Networks: A popular architecture for human pose estimation, using symmetric hourglass-shaped layers to capture keypoints at multiple scales.

OpenPose: A well-known algorithm that can detect human poses in real-time, utilizing multi-stage CNNs to detect keypoints.

Training:

The model is trained to predict keypoint locations, often by minimizing the distance between predicted and true keypoints in an image.

Loss Function: Typically a combination of heatmap regression loss and geometric constraints (such as joint angles).

Evaluation:

Accuracy Metrics: Mean Average Precision (mAP) or Percentage of Correct Keypoints (PCK) are commonly used to evaluate the performance of pose estimation models.

Visual Inspection: Overlaying predicted keypoints on the input image to assess how accurately the model identifies joint positions.

Deployment:

Real-time pose tracking using video feeds.

Integration into applications using frameworks like OpenCV for pose detection in live video or augmented reality apps.

Tools & Libraries:

Python, TensorFlow, Keras, or PyTorch

OpenCV for image processing and visualization

MediaPipe for real-time pose detection

Applications:

Sports Analytics: Analyze athlete movement and performance.

Healthcare: Assist in physical therapy and rehabilitation by tracking patient movements.

Human-Computer Interaction (HCI): Enable gesture recognition and control in VR/AR applications.

Surveillance: Monitor human activity and behavior in security footage.