How to Perform Object Tracking Using Roboflow Inference API

Object tracking has revolutionized how we monitor and follow individual objects across video frames, and with the help of the Roboflow Inference API, this task has never been easier. In this article, we’ll break down the steps to make your own object tracking setup using Roboflow, alongside leveraging the power of CLIP and Deep SORT for efficient tracking.

What is Object Tracking?

At its core, object tracking refers to the technique of detecting and following specific objects in successive frames of video. Think of it as having a keen eye at a zoo; you’re able to follow a particular animal roving through the enclosures. In this case, the enclosure is the video frame and the animal is your object of interest.

Getting Started with the Roboflow Inference API

Before diving into the technical details, ensure you have the prerequisite knowledge on:

• Object detection models
• Image classification

We utilize Zero-Shot CLIP Object Tracking in conjunction with Roboflow to create a seamless process for tracking objects.

Training Your Model

To harness the Roboflow Inference API, follow these steps:

1. Upload, annotate, and train your model using Roboflow Train.
2. Your model will subsequently be hosted on an inference URL.

If you decide to use YOLO models for detection (YOLOv5 or YOLOv7), follow the respective tutorials:

• Train YOLOv7 on Custom Data
• Train YOLOv5 on Custom Data

Performing Object Tracking

Once your model is ready, it’s time to perform object tracking. The step-by-step process is as follows:

1. Clone the required repositories:

git clone https://github.com/roboflow-ai/zero-shot-object-tracking
cd zero-shot-object-tracking
git clone https://github.com/openai/CLIP.git CLIP-repo
cp -r CLIP-repo/clip .

2. Install the necessary requirements based on your Python version:
• For Python 3.7+:

bash
pip install --upgrade pip
pip install -r requirements.txt

• For Anaconda Python 3.8:

conda install pytorch torchvision torchaudio -c pytorch
conda install ftfy regex tqdm requests pandas seaborn
pip install opencv pycocotools tensorflow

3. Run the script with any of the following engines:
• Using Roboflow:

bash
python clip_object_tracker.py --source data/video/fish.mp4 --url https://detect.roboflow.com/playing-cards-ow27d1 --api_key ROBOFLOW_API_KEY --info

• Using YOLOv7:

bash
python clip_object_tracker.py --weights models/yolov7.pt --source data/video/fish.mp4 --detection-engine yolov7 --info

• Using YOLOv5:

bash
python clip_object_tracker.py --weights models/yolov5s.pt --source data/video/fish.mp4 --detection-engine yolov5 --info

Troubleshooting

If you encounter issues during setup or execution, consider the following:

• Ensure that all dependencies are correctly installed.
• Verify that the provided API key from Roboflow is valid.
• Check that your video source is accessible and correctly specified.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following the above steps, you can successfully implement object tracking using the Roboflow Inference API. This powerful approach allows you to track numerous objects utilizing a combination of state-of-the-art tools, thus enhancing your projects and applications.

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.