YOLOv8中引入AIFI（Attention-based Intrascale Feature Interaction）源码

# Multi-Object Tracking with Ultralytics YOLO <img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png"> Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics. ## Why Choose Ultralytics YOLO for Object Tracking? The output from Ultralytics trackers is consistent with standard object detection but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs: - **Efficiency:** Process video streams in real-time without compromising accuracy. - **Flexibility:** Supports multiple tracking algorithms and configurations. - **Ease of Use:** Simple Python API and CLI options for quick integration and deployment. - **Customizability:** Easy to use with custom trained YOLO models, allowing integration into domain-specific applications. **Video Tutorial:** [Object Detection and Tracking with Ultralytics YOLOv8](https://www.youtube.com/embed/hHyHmOtmEgs?si=VNZtXmm45Nb9s-N-). ## Features at a Glance Ultralytics YOLO extends its object detection features to provide robust and versatile object tracking: - **Real-Time Tracking:** Seamlessly track objects in high-frame-rate videos. - **Multiple Tracker Support:** Choose from a variety of established tracking algorithms. - **Customizable Tracker Configurations:** Tailor the tracking algorithm to meet specific requirements by adjusting various parameters. ## Available Trackers Ultralytics YOLO supports the following tracking algorithms. They can be enabled by passing the relevant YAML configuration file such as `tracker=tracker_type.yaml`: - [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - Use `botsort.yaml` to enable this tracker. - [ByteTrack](https://github.com/ifzhang/ByteTrack) - Use `bytetrack.yaml` to enable this tracker. The default tracker is BoT-SORT. ## Tracking To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLOv8n, YOLOv8n-seg and YOLOv8n-pose. #### Python ```python from ultralytics import YOLO # Load an official or custom model model = YOLO("yolov8n.pt") # Load an official Detect model model = YOLO("yolov8n-seg.pt") # Load an official Segment model model = YOLO("yolov8n-pose.pt") # Load an official Pose model model = YOLO("path/to/best.pt") # Load a custom trained model # Perform tracking with the model results = model.track( source="https://youtu.be/LNwODJXcvt4", show=True ) # Tracking with default tracker results = model.track( source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml" ) # Tracking with ByteTrack tracker ``` #### CLI ```bash # Perform tracking with various models using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" # Official Detect model yolo track model=yolov8n-seg.pt source="https://youtu.be/LNwODJXcvt4" # Official Segment model yolo track model=yolov8n-pose.pt source="https://youtu.be/LNwODJXcvt4" # Official Pose model yolo track model=path/to/best.pt source="https://youtu.be/LNwODJXcvt4" # Custom trained model # Track using ByteTrack tracker yolo track model=path/to/best.pt tracker="bytetrack.yaml" ``` As can be seen in the above usage, tracking is available for all Detect, Segment and Pose models run on videos or streaming sources. ## Configuration ### Tracking Arguments Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](https://docs.ultralytics.com/modes/predict/) model page. #### Python ```python from ultralytics import YOLO # Configure the tracking parameters and run the tracker model = YOLO("yolov8n.pt") results = model.track( source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True ) ``` #### CLI ```bash # Configure tracking parameters and run the tracker using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show ``` ### Tracker Selection Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file (for example, `custom_tracker.yaml`) from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modify any configurations (except the `tracker_type`) as per your needs. #### Python ```python from ultralytics import YOLO # Load the model and run the tracker with a custom configuration file model = YOLO("yolov8n.pt") results = model.track( source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml" ) ``` #### CLI ```bash # Load the model and run the tracker with a custom configuration file using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml' ``` For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) page. ## Python Examples ### Persisting Tracks Loop Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker than the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image. #### Python ```python import cv2 from ultralytics import YOLO # Load the YOLOv8 model model = YOLO("yolov8n.pt") # Open the video file video_path = "path/to/video.mp4" cap = cv2.VideoCapture(video_path) # Loop through the video frames while cap.isOpened(): # Read a frame from the video success, frame = cap.read() if success: # Run YOLOv8 tracking on the frame, persisting tracks between frames results = model.track(frame, persist=True) # Visualize the results on the frame annotated_frame = results[0].plot() # Display the annotated frame cv2.imshow("YOLOv8 Tracking", annotated_frame) # Break the loop if 'q' is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break else: # Break the loop if the end of the video is reached break # Release the video capture object and close the display window cap.release() cv2.destroyAllWindows() ``` Please note the change from `model(frame)` to `model.track(frame)`, which enables object tracking instead of simple detection. This modified script will run the tracker on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'. ### Plotting Tracks Over Time Visualizing object tracks over consecutive frames can provide valuable insights into the movement patterns and behavior of detected objects within a video. With Ultralytics YOLOv8, plotting these tracks is a seamless and efficient process. In the following example, we demonstrate how to utilize YOLOv8's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects. #### Python ```python from collections import defaultdict import cv2 import numpy as np from ultralytics import YOLO # Load the YOLOv8 model model = YOLO("yolov8n.pt") # Open the video file v