Therapist and Child Detection and Tracking assignment, here's a high-level approach to help guide you:

1. Person Detection Model

Models to Use: YOLOv8 or YOLOv7 (You Only Look Once) for object detection, which is well-suited for detecting multiple classes like children and adults. OpenPose or DeepSORT for multi-person tracking.

2. Tracking Algorithm

DeepSORT (Simple Online and Realtime Tracking with a deep association metric) or ByteTrack are good choices for assigning unique IDs and tracking re-entries, as they handle occlusion well. Re-identification (ReID) models can also help assign consistent IDs when individuals re-enter the frame after leaving.

3. Implementation Steps

Step 1: Load Pre-trained Model

Load a pre-trained YOLOv8 model for detection of people (children and adults). You can train the model further with a custom dataset if needed.

Step 2: Detection Pipeline

Apply the object detection model to each frame of the video to detect persons. Use a confidence threshold to filter out irrelevant objects.

Step 3: Tracking Algorithm

Use DeepSORT or ByteTrack to assign unique IDs and track persons across frames. These algorithms use bounding box coordinates from the detection model to track objects. The tracker will reassign IDs when individuals re-enter the frame after occlusion.

Step 4: Post-Occlusion Handling

Ensure that IDs are correctly reassigned when persons re-enter after occlusion. DeepSORT or ByteTrack handle this by using a combination of appearance features and motion predictions.

Step 5: Video Output

Annotate the output video frames with bounding boxes, labels (e.g., child, therapist), and the unique ID for each person. Use OpenCV to overlay bounding boxes and save the resulting video.

4. Optimization

Use techniques like non-maximum suppression (NMS) to avoid redundant detections. Consider optimizing for long videos by using frame skipping or resizing to reduce computational load without losing accuracy.

5. Testing on Provided Video

Download the provided test videos and run your inference pipeline to ensure predictions are overlaid on the videos as expected.