Object Tracking and Location Reporting using YOLO and OpenCV

Project Overview

This project leverages the YOLOv8 object detection model and a custom tracking algorithm to process video feeds or sequences of images. The detected objects are tracked across frames, and their GPS locations are reported to a ground station. The project demonstrates the integration of computer vision and real-time location reporting for applications such as surveillance, autonomous vehicles, and more.

Key Features

• Object Detection: Uses YOLOv8 to detect objects in video frames or images.
• Object Tracking: Tracks detected objects across multiple frames.
• Location Reporting: Reports the GPS location of tracked objects to a ground station.
• Real-time Processing: Processes video feed in real-time and displays results with bounding boxes and object IDs.

Table of Contents

• Installation
• Usage
• Code Explanation
  • Loading the YOLO Model
  • Processing Frames
  • Video and Image Processing
• License
• Contact

Installation

1. Clone the repository: ```bash git clone https://github.com/username/YOLO_Object_Tracking.git cd YOLO_Object_Tracking ```

2. Install the required dependencies: ```bash pip install -r requirements.txt ```

3. Download the YOLOv8 model weights and place them in the project directory: ```bash

   Download from the official YOLO website or use the command

   wget https://path.to/yolov8s.pt ```

Usage

1. Ensure you have your video file or image folder in the specified path.

2. Run the script to process video feed or images.

   For video feed: ```python process_video('path/to/your/video.mp4') ```

   For image sequences: ```python process_images('/path/to/your/image_folder') ```

Code Explanation

Loading the YOLO Model

The YOLOv8 model is loaded with the following line: ```python model = YOLO('yolov8s.pt') ``` The model weights should be placed in the project directory.

Processing Frames

The `process_frame` function is responsible for resizing frames, running the YOLO model for detection, and tracking the detected objects.

```python def process_frame(frame): frame = cv2.resize(frame, (1020, 500)) results = model.predict(frame) a = results[0].boxes.data px = pd.DataFrame(a).astype("float") list = [] for index, row in px.iterrows(): x1 = int(row[0]) y1 = int(row[1]) x2 = int(row[2]) y2 = int(row[3]) d = int(row[5]) if d >= len(class_list): print(f"Warning: Detected class ID {d} is out of range for class_list.") continue # Skip this detection if class ID is out of range c = class_list[d] list.append([x1, y1, x2, y2, d]) # Include the class id in the list bbox_id = tracker.update([bbox[:4] for bbox in list]) # Pass only bbox coordinates for bbox in bbox_id: x3, y3, x4, y4, obj_id = bbox cx = (x3 + x4) // 2 cy = (y3 + y4) // 2 cv2.rectangle(frame, (x3, y3), (x4, y4), (0, 0, 255), 1) cv2.circle(frame, (cx, cy), 3, (255, 0, 255), -1) cv2.putText(frame, str(obj_id), (x3, y3), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1) if obj_id not in reported_ids: send_location(obj_id) # Function to send GPS location reported_ids.append(obj_id) else: print(obj_id, "- already sent") return frame ```

Video and Image Processing

• Video Feed: ```python def process_video(video_path): cap = cv2.VideoCapture(video_path) while True: ret, frame = cap.read() if not ret: break frame = process_frame(frame) cv2.imshow("RGB", frame) if cv2.waitKey(1) & 0xFF == 27: break cap.release() cv2.destroyAllWindows() ```

• Image Sequence: ```python def process_images(image_folder): images = [img for img in os.listdir(image_folder) if img.endswith(('.png', '.jpg', '.jpeg'))] images.sort() for image in images: frame = cv2.imread(os.path.join(image_folder, image)) frame = process_frame(frame) cv2.imshow("RGB", frame) if cv2.waitKey(1) & 0xFF == 27: break cv2.destroyAllWindows() ```

Tracker Instance

A `Tracker` instance is used to keep track of detected objects across frames: ```python tracker = Tracker() reported_ids = [] ```

Mouse Callback Function

The `RGB` function is used to capture mouse movements over the display window: ```python def RGB(event, x, y, flags, param): if event == cv2.EVENT_MOUSEMOVE: colorsBGR = [x, y] print(colorsBGR) cv2.namedWindow('RGB') cv2.setMouseCallback('RGB', RGB) ```

Sending Location

The `send_location` function implements the logic to send GPS locations to the ground station: ```python def send_location(obj_id): print("location sent - ", obj_id) # Implement the logic to send GPS location to the ground station pass ```

License

This project is licensed under the MIT License.

Contact

For any questions or contributions, please contact [yashgolani2004gmail.com].