Multi-object tracking is a fascinating field in computer vision that enables us to track and identify multiple entities within an environment. In this article, we will walk through the steps of using various multi-object tracking algorithms in Python, specifically using the motrackers library. We’ll make this process user-friendly, offer troubleshooting tips, and add some creative analogies to help you understand better.

Available Multi-Object Trackers

• CentroidTracker
• IOUTracker
• CentroidKF_Tracker
• SORT

Available OpenCV-based Object Detectors

• detector.TF_SSDMobileNetV2
• detector.Caffe_SSDMobileNet
• detector.YOLOv3

Installation

To get started, you need to install OpenCV (version 3.4.3 or later). You can do this via pip as shown below:

pip install opencv-python

Next, you can install the motrackers package using:

pip install motrackers

Alternatively, you can clone the GitHub repository and install it directly:

git clone https://github.com/adipandasmulti-object-tracker
cd multi-object-tracker
pip install [-e] .

Note: To use the OpenCV DNN-based object detection modules with GPU, you might need to compile a CUDA-enabled version of OpenCV from source. Helpful links for building OpenCV can be found here and here.

How to Use Multi-Object Trackers?

Now that we have the tools installed, let’s dive into how to use them. Think of the code as a recipe in a kitchen where each ingredient has a specific role. Here’s a simple template to guide you:

from motrackers import CentroidTracker  # or IOUTracker, CentroidKF_Tracker, SORT

input_data = ...
detector = ...
tracker = CentroidTracker(...)  # or IOUTracker(...), CentroidKF_Tracker(...), SORT(...)

while True:
    done, image = read(input_data)
    if done:
        break

    detection_bboxes, detection_confidences, detection_class_ids = detector.detect(image)

    output_tracks = tracker.update(detection_bboxes, detection_confidences, detection_class_ids)

    for track in output_tracks:
        frame, id, bb_left, bb_top, bb_width, bb_height, confidence, x, y, z = track
        assert len(track) == 10
        print(track)

In this analogy, the CentroidTracker acts like a chef, mixing all the ingredients (or detections) together to create a harmonious dish (the complete tracking output). Each tracking object is treated like an ingredient, with their precise measurements being akin to the values passed in the code.

Pretrained Object Detection Models

To get the best results, you need pretrained weights for the neural-network models. You can download them via shell scripts provided in the repository here. For further details, refer to DOWNLOAD_WEIGHTS.md.

Troubleshooting

If you encounter issues while implementing the multi-object tracker, here are some troubleshooting tips:

• Ensure that all dependencies are installed properly.
• Check that the input data is in the correct format.
• If using GPU, verify that your system has the correct CUDA version and that OpenCV is compiled with CUDA support.
• Look for typos or misconfigurations in your model paths or parameters.

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Conclusion

With these steps, you should be well on your way to implementing multi-object tracking in Python. Remember, at fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.