`ultralytics 8.3.40` new `TrackZone` Solution (#17918)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/3305/head^2 v8.3.40
Muhammad Rizwan Munawar
2 days ago
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description: Discover how TrackZone leverages Ultralytics YOLO11 to precisely track objects within specific zones, enabling real-time insights for crowd analysis, surveillance, and targeted monitoring. |
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keywords: TrackZone, object tracking, YOLO11, Ultralytics, real-time object detection, AI, deep learning, crowd analysis, surveillance, zone-based tracking, resource optimization |
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--- |
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# TrackZone using Ultralytics YOLO11 |
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## What is TrackZone? |
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TrackZone specializes in monitoring objects within designated areas of a frame instead of the whole frame. Built on [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/), it integrates object detection and tracking specifically within zones for videos and live camera feeds. YOLO11's advanced algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) technologies make it a perfect choice for real-time use cases, offering precise and efficient object tracking in applications like crowd monitoring and surveillance. |
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## Advantages of Object Tracking in Zones (TrackZone) |
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- **Targeted Analysis:** Tracking objects within specific zones allows for more focused insights, enabling precise monitoring and analysis of areas of interest, such as entry points or restricted zones. |
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- **Improved Efficiency:** By narrowing the tracking scope to defined zones, TrackZone reduces computational overhead, ensuring faster processing and optimal performance. |
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- **Enhanced Security:** Zonal tracking improves surveillance by monitoring critical areas, aiding in the early detection of unusual activity or security breaches. |
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- **Scalable Solutions:** The ability to focus on specific zones makes TrackZone adaptable to various scenarios, from retail spaces to industrial settings, ensuring seamless integration and scalability. |
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## Real World Applications |
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| Agriculture | Transportation | |
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| :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------: | |
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|  |  | |
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| Plants Tracking in Field Using Ultralytics YOLO11 | Vehicles Tracking on Road using Ultralytics YOLO11 | |
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!!! example "TrackZone using YOLO11 Example" |
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=== "CLI" |
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```bash |
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# Run a trackzone example |
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yolo solutions trackzone show=True |
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# Pass a source video |
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yolo solutions trackzone show=True source="path/to/video/file.mp4" |
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# Pass region coordinates |
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yolo solutions trackzone show=True region=[(150, 150), (1130, 150), (1130, 570), (150, 570)] |
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``` |
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=== "Python" |
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```python |
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import cv2 |
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from ultralytics import solutions |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Define region points |
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region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)] |
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# Video writer |
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video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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# Init TrackZone (Object Tracking in Zones, not complete frame) |
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trackzone = solutions.TrackZone( |
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show=True, # Display the output |
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region=region_points, # Pass region points |
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model="yolo11n.pt", # You can use any model that Ultralytics support, i.e. YOLOv9, YOLOv10 |
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# line_width=2, # Adjust the line width for bounding boxes and text display |
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# classes=[0, 2], # If you want to count specific classes i.e. person and car with COCO pretrained model. |
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) |
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# Process video |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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im0 = trackzone.trackzone(im0) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Argument `TrackZone` |
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Here's a table with the `TrackZone` arguments: |
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| Name | Type | Default | Description | |
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| ------------ | ------ | ---------------------------------------------------- | ---------------------------------------------------- | |
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| `model` | `str` | `None` | Path to Ultralytics YOLO Model File | |
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| `region` | `list` | `[(150, 150), (1130, 150), (1130, 570), (150, 570)]` | List of points defining the object tracking region. | |
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| `line_width` | `int` | `2` | Line thickness for bounding boxes. | |
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| `show` | `bool` | `False` | Flag to control whether to display the video stream. | |
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### Arguments `model.track` |
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{% include "macros/track-args.md" %} |
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## FAQ |
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### How do I track objects in a specific area or zone of a video frame using Ultralytics YOLO11? |
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Tracking objects in a defined area or zone of a video frame is straightforward with Ultralytics YOLO11. Simply use the command provided below to initiate tracking. This approach ensures efficient analysis and accurate results, making it ideal for applications like surveillance, crowd management, or any scenario requiring zonal tracking. |
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```bash |
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yolo solutions trackzone source="path/to/video/file.mp4" show=True |
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``` |
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### How can I use TrackZone in Python with Ultralytics YOLO11? |
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With just a few lines of code, you can set up object tracking in specific zones, making it easy to integrate into your projects. |
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```python |
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import cv2 |
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from ultralytics import solutions |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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# Define region points |
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region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)] |
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# Video writer |
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video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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# Init TrackZone (Object Tracking in Zones, not complete frame) |
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trackzone = solutions.TrackZone( |
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show=True, # Display the output |
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region=region_points, # Pass region points |
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model="yolo11n.pt", |
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) |
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# Process video |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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im0 = trackzone.trackzone(im0) |
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video_writer.write(im0) |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### How do I configure the zone points for video processing using Ultralytics TrackZone? |
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Configuring zone points for video processing with Ultralytics TrackZone is simple and customizable. You can directly define and adjust the zones through a Python script, allowing precise control over the areas you want to monitor. |
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```python |
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# Define region points |
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region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)] |
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# Init TrackZone (Object Tracking in Zones, not complete frame) |
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trackzone = solutions.TrackZone( |
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show=True, # Display the output |
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region=region_points, # Pass region points |
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) |
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``` |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import cv2 |
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import numpy as np |
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from ultralytics.solutions.solutions import BaseSolution |
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from ultralytics.utils.plotting import Annotator, colors |
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class TrackZone(BaseSolution): |
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""" |
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A class to manage region-based object tracking in a video stream. |
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This class extends the BaseSolution class and provides functionality for tracking objects within a specific region |
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defined by a polygonal area. Objects outside the region are excluded from tracking. It supports dynamic initialization |
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of the region, allowing either a default region or a user-specified polygon. |
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Attributes: |
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region (ndarray): The polygonal region for tracking, represented as a convex hull. |
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Methods: |
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trackzone: Processes each frame of the video, applying region-based tracking. |
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Examples: |
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>>> tracker = TrackZone() |
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>>> frame = cv2.imread("frame.jpg") |
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>>> processed_frame = tracker.trackzone(frame) |
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>>> cv2.imshow("Tracked Frame", processed_frame) |
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""" |
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def __init__(self, **kwargs): |
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"""Initializes the TrackZone class for tracking objects within a defined region in video streams.""" |
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super().__init__(**kwargs) |
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default_region = [(150, 150), (1130, 150), (1130, 570), (150, 570)] |
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self.region = cv2.convexHull(np.array(self.region or default_region, dtype=np.int32)) |
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def trackzone(self, im0): |
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""" |
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Processes the input frame to track objects within a defined region. |
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This method initializes the annotator, creates a mask for the specified region, extracts tracks |
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only from the masked area, and updates tracking information. Objects outside the region are ignored. |
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Args: |
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im0 (numpy.ndarray): The input image or frame to be processed. |
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Returns: |
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(numpy.ndarray): The processed image with tracking id and bounding boxes annotations. |
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Examples: |
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>>> tracker = TrackZone() |
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>>> frame = cv2.imread("path/to/image.jpg") |
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>>> tracker.trackzone(frame) |
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""" |
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self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator |
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# Create a mask for the region and extract tracks from the masked image |
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masked_frame = cv2.bitwise_and(im0, im0, mask=cv2.fillPoly(np.zeros_like(im0[:, :, 0]), [self.region], 255)) |
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self.extract_tracks(masked_frame) |
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cv2.polylines(im0, [self.region], isClosed=True, color=(255, 255, 255), thickness=self.line_width * 2) |
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# Iterate over boxes, track ids, classes indexes list and draw bounding boxes |
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for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss): |
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self.annotator.box_label(box, label=f"{self.names[cls]}:{track_id}", color=colors(track_id, True)) |
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self.display_output(im0) # display output with base class function |
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return im0 # return output image for more usage |
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