Deep SORT is a sophisticated algorithm designed for real-time object tracking, leveraging powerful machine learning techniques to enhance its performance significantly. This blog serves as a user-friendly guide to getting started with Deep SORT, troubleshooting common issues, and understanding its implementation intricacies through relatable analogies.

Introduction

This version of Deep SORT has been enhanced to adapt efficiently for real-time tracking applications. It is an adaptation derived from the official repository of Simple Online and Realtime Tracking with a Deep Association Metric, also known as Deep SORT. For further technical details, you can refer to their research paper.

Dependencies

The software requires several dependencies which can be installed via requirements.txt. Below are the main dependencies you need to set up:

• Python3
• NumPy: pip install numpy
• SciPy: pip install scipy
• OpenCV: pip install opencv-python
• (Optional) Pytorch and Torchvision for default MobileNetV2 Embedder:
  • pip install torch torchvision
• (Optional) TensorFlow 2+:
  • pip install tensorflow
• (Optional) Torchreid Embedder: Follow the installation guide from Torchreid.
• (Optional) CLIP Embedder: pip install git+https://github.com/openai/CLIP.git

Installation

You can install Deep SORT using any of the following methods:

• From PyPI: pip3 install deep-sort-realtime or python3 -m pip install deep-sort-realtime
• Clone the repository and install:

  bash
  cd deep_sort_realtime
  pip3 install .
  

• Alternatively, download the .whl file from the releases page.

Running Deep SORT

Here’s a simple example to get you started:

python
from deep_sort_realtime.deepsort_tracker import DeepSort

tracker = DeepSort(max_age=5)
bbs = object_detector.detect(frame)
tracks = tracker.update_tracks(bbs, frame=frame) 

for track in tracks:
    if not track.is_confirmed():
        continue
    track_id = track.track_id
    ltrb = track.to_ltrb()

An Analogy to Understand the Code

Think of Deep SORT as a high-tech security team at a mall. Each security member (the tracker) keeps an eye on shoppers (the detected objects) moving around. Each shopper has a unique ID (track ID) and a specific route they take (bounding values). Initially, the security member receives a list of people entering (bbs) and has a timeframe (max_age) to keep track of them before forgetting. If a shopper remains in the vicinity, the guard updates their record (update_tracks) to include new details – such as where they’re heading in the mall (frame). Only confirmed shoppers stay on the guard’s radar.

Troubleshooting

If you encounter issues while setting up or using Deep SORT, consider the following troubleshooting tips:

• Ensure you have installed all specified dependencies without errors.
• Check for compatibility issues between the Python version and the packages.
• If you get errors about missing modules, double-check your package installations.
• Review the configuration settings for the embedder to ensure correct integration.
• Look into the repository’s issues page for community support.

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Advanced Features

Deep SORT allows further customization and functionality such as:

• Custom logic by subclassing the Track class.
• Handling polygon-based detections for complex shapes.
• Adding supplementary information to tracks.

Conclusion

Deep SORT provides robust capabilities for real-time object tracking. By integrating various dependencies and understanding the setup process, you can implement effective tracking systems for different applications, from surveillance to sports analytics.

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.