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# Run yolov4 deep sort object tracker on webcam (set video flag to 0) # Run yolov4 deep sort object tracker on video # Convert darknet weights to tensorflow model Then all we need to do is run the object_tracker.py script to run our object tracker with YOLOv4, DeepSort and TensorFlow. weights into the corresponding TensorFlow model which will be saved to a checkpoints folder. To implement the object tracking using YOLOv4, first we convert the. If you want to use yolov4-tiny.weights, a smaller model that is faster at running detections but less accurate, download file here: Running the Tracker with YOLOv4 Download pre-trained yolov4.weights file: Ĭopy and paste yolov4.weights from your downloads folder into the 'data' folder of this repository. For easy demo purposes we will use the pre-trained weights for our tracker. There exists an official pre-trained YOLOv4 object detector model that is able to detect 80 classes. Our object tracker uses YOLOv4 to make the object detections, which deep sort then uses to track. Downloading Official YOLOv4 Pre-trained Weights

Make sure to use CUDA Toolkit version 10.1 as it is the proper version for the TensorFlow version used in this repository.

Pip install -r requirements-gpu.txt Nvidia Driver (For GPU, if you are not using Conda Environment and haven't set up CUDA yet)

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