OpenCV Person Detection and Tracking: A Guide

• Introduction
• Step-by-Step Guide
• Code Example
• Additional Notes
• Summary
• Conclusion
• References

This article provides a step-by-step guide on how to perform people detection, tracking, and re-identification in video streams or images using Python and OpenCV. We will utilize the Histogram of Oriented Gradients (HOG) descriptor for detection, the CSRT algorithm for tracking, and deep learning models for re-identification.

1. Import necessary libraries:

import cv2

2. Load pre-trained HOG descriptor:

hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

3. Open video stream or load image:

video_capture = cv2.VideoCapture(0)  # For webcam
# or
image = cv2.imread('image.jpg')

4. Detect people in each frame/image:

(rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4), padding=(8, 8), scale=1.05)

5. Draw bounding boxes around detected people:

for (x, y, w, h) in rects:
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

6. Display the output:

cv2.imshow('People Detection', frame)
cv2.waitKey(1)

7. For tracking people across frames, use object tracking algorithms like CSRT:

tracker = cv2.TrackerCSRT_create()
tracker.init(frame, bounding_box)

8. Update tracker in subsequent frames:

(success, box) = tracker.update(frame)

9. For person re-identification, use deep learning models and libraries like OpenCV's person_reid.py:

model = cv2.dnn.readNetFromTorch('person_reid.pth')
features = model.forward(person_image)

10. Calculate similarity scores between features to identify the same person across cameras or time.

This Python code uses OpenCV to perform real-time people detection and tracking in a video stream. It utilizes a pre-trained HOG descriptor for person detection and optionally employs a CSRT tracker to follow detected individuals across frames. The code captures video from the default camera, detects people within each frame, and draws bounding boxes around them. If a tracker is initialized, it updates the position of the tracked person. The resulting video output displays the original frames with bounding boxes highlighting detected people. The program terminates when the 'q' key is pressed.

 # Import necessary libraries
 import cv2

 # Load pre-trained HOG descriptor
 hog = cv2.HOGDescriptor()
 hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

 # Open video stream
 video_capture = cv2.VideoCapture(0)

 # Initialize tracker (optional)
 tracker = None

 while True:
     # Read frame from video stream
     ret, frame = video_capture.read()

     # Detect people in the frame
     (rects, weights) = hog.detectMultiScale(frame, winStride=(4, 4),
                                             padding=(8, 8), scale=1.05)

     # Draw bounding boxes around detected people
     for (x, y, w, h) in rects:
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

         # Initialize tracker if not already initialized
         if tracker is None:
             tracker = cv2.TrackerCSRT_create()
             tracker.init(frame, (x, y, w, h))

     # Update tracker if initialized
     if tracker is not None:
         (success, box) = tracker.update(frame)
         if success:
             (x, y, w, h) = [int(v) for v in box]
             cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)

     # Display the output
     cv2.imshow('People Detection', frame)

     # Break the loop if 'q' key is pressed
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break

 # Release the video capture object
 video_capture.release()
 cv2.destroyAllWindows()

Explanation:

1. Import necessary libraries: This line imports the OpenCV library.
2. Load pre-trained HOG descriptor: This section loads the pre-trained HOG descriptor for person detection.
3. Open video stream: This line initializes the video capture object to capture video from the default camera.
4. Initialize tracker (optional): This line initializes a tracker object (CSRT tracker in this case) to track detected people across frames.
5. Read frame from video stream: This line reads a frame from the video stream.
6. Detect people in the frame: This line uses the detectMultiScale method of the HOG descriptor to detect people in the current frame.
7. Draw bounding boxes around detected people: This loop iterates over the detected people and draws bounding boxes around them.
8. Initialize tracker if not already initialized: If a tracker is not already initialized, this section initializes it with the bounding box of the first detected person.
9. Update tracker if initialized: If a tracker is initialized, this section updates its state based on the current frame.
10. Display the output: This line displays the frame with detected people and tracked objects.
11. Break the loop if 'q' key is pressed: This line checks if the 'q' key is pressed and breaks the loop if it is.
12. Release the video capture object: This line releases the video capture object and closes all windows.

Note:

• This code example demonstrates basic person detection and tracking using HOG descriptor and CSRT tracker.
• For person re-identification, you would need to implement deep learning models and libraries like OpenCV's person_reid.py as mentioned in the article.
• You can adjust the parameters of detectMultiScale method to fine-tune the detection accuracy.
• You can use different object tracking algorithms based on your requirements.
• This code assumes you have a webcam connected to your computer. If not, you can replace 0 in cv2.VideoCapture(0) with the path to your video file.

## Additional Notes


**General:**

* **Performance:** HOG + SVM is relatively fast for person detection, but can be computationally expensive for real-time applications, especially with high-resolution video. Consider downsampling frames or using a more efficient detector if needed.
* **Accuracy:** Detection accuracy depends heavily on the quality of the video stream, lighting conditions, and the diversity of the training data used for the HOG descriptor. 
* **False Positives:**  The algorithm might generate false positives, detecting objects that are not people. Fine-tuning parameters and potentially training a custom HOG descriptor on relevant data can help mitigate this.

**Detection (HOG):**

* **`detectMultiScale` parameters:** Experiment with different values for `winStride`, `padding`, and `scale` to optimize for your specific scenario (e.g., smaller stride for detecting smaller people).
* **Custom HOG Descriptor:** For improved accuracy on specific datasets or scenarios, consider training your own HOG descriptor using positive and negative samples.

**Tracking (CSRT):**

* **Tracker Initialization:** The tracker needs to be re-initialized whenever the person is lost or a new person enters the scene. This can be done by detecting people in each frame and associating them with existing tracks.
* **Occlusion Handling:**  The tracker might lose track of a person if they are occluded for a long time. Implement strategies like track prediction or re-identification to handle occlusions.
* **Other Tracking Algorithms:**  Explore other tracking algorithms like KCF, MIL, or deep learning-based trackers for potentially better performance depending on your needs.

**Re-Identification:**

* **Deep Learning Models:**  Pre-trained models for person re-identification are available in libraries like OpenCV. You can also fine-tune these models on your own dataset for improved accuracy.
* **Feature Extraction:**  The `person_reid.py` example in OpenCV demonstrates how to extract features from person images using a deep learning model. These features can then be used for similarity comparison.
* **Similarity Metrics:**  Use appropriate distance metrics like cosine similarity or Euclidean distance to compare feature vectors and determine if two detections belong to the same person.

**Additional Features:**

* **Counting People:**  Implement a counter to track the number of people entering or leaving a specific area.
* **Activity Recognition:** Analyze the movement patterns of tracked people to recognize actions like walking, running, or falling.
* **Privacy Considerations:**  Be mindful of privacy concerns when implementing people tracking systems. Consider anonymizing or discarding personal data appropriately. 

## Summary


This code snippet demonstrates how to detect and track people in images or videos using OpenCV in Python. 

**Key Steps:**

1. **Load pre-trained HOG descriptor:** Utilize OpenCV's pre-trained Histogram of Oriented Gradients (HOG) descriptor and Support Vector Machine (SVM) classifier for efficient pedestrian detection.
2. **Capture input:** Access live video streams from webcams or load static images from files.
3. **Detect people:** Apply the HOG descriptor to identify potential human figures within each frame or image, generating bounding boxes around them.
4. **Visualize detections:** Draw rectangles around detected individuals in real-time, providing a visual representation of the algorithm's output.
5. **Track individuals:** Implement object tracking algorithms like CSRT (Channel and Spatial Reliability Tracking) to follow specific people across consecutive frames, maintaining their identities even when they move.
6. **Re-identify individuals:** For advanced scenarios, leverage deep learning models and libraries like OpenCV's `person_reid.py` to extract unique features from detected individuals. This enables re-identification of the same person across different cameras or time periods by comparing feature similarity scores.

**Libraries Used:**

- `cv2` (OpenCV)

**Key Functions:**

- `cv2.HOGDescriptor()`
- `cv2.HOGDescriptor_getDefaultPeopleDetector()`
- `hog.detectMultiScale()`
- `cv2.TrackerCSRT_create()`
- `cv2.dnn.readNetFromTorch()`

This approach provides a foundation for building applications requiring pedestrian detection, tracking, and re-identification, such as security systems, crowd analysis, and retail analytics. 

## Conclusion

This article explored techniques for people detection, tracking, and re-identification in video streams or images using Python and OpenCV. By leveraging the HOG descriptor for detection, the CSRT algorithm for tracking, and deep learning models for re-identification, developers can build robust applications for various purposes. The code example provided offers a starting point for implementing these techniques, while the discussion of parameters, alternative algorithms, and potential challenges empowers readers to further refine and adapt the code to their specific needs. As computer vision technology continues to advance, we can expect even more sophisticated and efficient methods for people detection and tracking to emerge, opening up new possibilities in fields like security, retail, and human-computer interaction. 

## References

* ![Real-time Human Detection with OpenCV](https://cynapps-thedatafrog.s3.amazonaws.com/media/images/Screen-Shot-2019-04-18-at-16.15.54.original.png) [Real-time Human Detection with OpenCV](https://thedatafrog.com/en/articles/human-detection-video/) | Let's use the HOG algorithm implemented in OpenCV to detect people in real time in a video stream!
* ![image processing - Using OpenCV to find people who wear a ...](https://cdn.sstatic.net/Sites/stackoverflow/Img/apple-touch-icon@2.png?v=73d79a89bded) [image processing - Using OpenCV to find people who wear a ...](https://stackoverflow.com/questions/15204391/using-opencv-to-find-people-who-wear-a-certain-hat) | Mar 4, 2013 ... The first link you posted describes a simple color-based detection. You can try that, but it will fail if there are other pixel clusters of similar color in ...
* ![OpenCV People Counter - PyImageSearch](https://pyimagesearch.com/wp-content/uploads/2018/08/opencv_people_counter_featured.jpg) [OpenCV People Counter - PyImageSearch](https://pyimagesearch.com/2018/08/13/opencv-people-counter/) | This tutorial will teach you how to build a people counter using OpenCV, Python, and object tracking algorithms. Using the OpenCV library we'll count the number of people moving "in" and "out" of a store.
* ![How do I track the same person across multiple cameras? : r ...](https://share.redd.it/preview/post/186lq7m) [How do I track the same person across multiple cameras? : r ...](https://www.reddit.com/r/computervision/comments/186lq7m/how_do_i_track_the_same_person_across_multiple/) | Posted by u/Puzzleheaded_Win9283 - 0 votes and 14 comments
* ![Person ReID in real time - Python - OpenCV](https://global.discourse-cdn.com/flex020/uploads/opencv/original/1X/76487ef436ed28b0102012160fcdae1be1efd4d4.png) [Person ReID in real time - Python - OpenCV](https://forum.opencv.org/t/person-reid-in-real-time/5795) | There are many ReID codes for OpenCV Python on the internet, but this is apparently the official one: opencv/person_reid.py at master · opencv/opencv · GitHub  My question is: how do I make it real time? That is, I indicate a query and in real time I traced the person by indicating an ID, and that recognizes who the person at all times (because it currently recognizes the person of the query, but only generates a photo with all the appearances that it has in The galery).  Can you help me?
* ![How to record the time of stay by detected people in a video? edit](https://answers.opencv.org/upfiles/14515120971628022.jpg) [How to record the time of stay by detected people in a video? edit](https://answers.opencv.org/question/80580/how-to-record-the-time-of-stay-by-detected-people-in-a-video/) | Dec 24, 2015 ... Maybe i will try to improve it or some ideas will raise. i tested it with 768x576.avi can be found OpenCV's \samples\data. ( tracking is not ...
* ![Seeking Guidance for Developing a Gender-Sensitive People ...](https://us1.discourse-cdn.com/flex020/uploads/opencv/original/1X/76487ef436ed28b0102012160fcdae1be1efd4d4.png) [Seeking Guidance for Developing a Gender-Sensitive People ...](https://forum.opencv.org/t/seeking-guidance-for-developing-a-gender-sensitive-people-counter-using-body-detection/17057) | Hello OpenCV Community,  I am currently working on a project to develop a people counter system that not only tracks entrances and exits but also incorporates gender recognition. The primary challenge I’m facing is the need to rely on body detection for this task, as the camera setup does not consistently capture faces with sufficient quality for reliable facial recognition. The pedestrians’ full bodies are visible, which I believe could be leveraged for gender determination.  Given these constr...
* ![key-point based detection vs HoG/Haar training - OpenCV Q&A Forum](/placeholder.png) [key-point based detection vs HoG/Haar training - OpenCV Q&A Forum](https://answers.opencv.org/question/29964/key-point-based-detection-vs-hoghaar-training/) | Mar 14, 2014 ... Also, people use different detectors specific for the object. For ... tracking algorithm here for example: Matrioska: Tracking By detection using ...
* ![Detecting small attachments on the body - Python - OpenCV](https://us1.discourse-cdn.com/flex020/uploads/opencv/original/2X/4/4f1ddfaea37159ae5eed0222506b656cef237ca1.png) [Detecting small attachments on the body - Python - OpenCV](https://forum.opencv.org/t/detecting-small-attachments-on-the-body/7339) | Hi,  I’m trying to detect some small attachments to the human body where the person wears minimal cloth. So, these attachments are directly on the skin.  I’ve tried color filtering to reveal these small attachments but it really did not work since color of the attachments are not too far away from what is present in the image  Also, Hough circles extracted too many circles.  Another thing, I tried is feature extraction by SIFT. It extracts too many features that are hard to choose from.  How can...