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yolov3, deep_sort and optical flow
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dataset add video Dec 14, 2018
deep_sort commit Dec 8, 2018
model_data commit Dec 8, 2018
optical_flow commit Dec 8, 2018
tools commit Dec 8, 2018
LICENSE commit Dec 14, 2018 commit Dec 8, 2018
yolo.pyc commit Dec 8, 2018


General Idea

A multi-object-tracking algorithm uses YOLO v3, deep_sort and optical flow based on Kanade–Lucas–Tomasi (KLT).


  1. YOLO v3 detection
  2. deep_sort tracker update
  3. optical flow tracker update


The code has been tested in python 3.5, ubuntu 16.04.

  1. tensorflow
  2. keras
  3. numpy
  4. sklearn
  5. scipy
  6. scikit-image
  7. opencv

How to run

  1. Download yolov3 model from YOLO website. Convert this model to a Keras model. For this project, we train a new yolov3 model and use Keras.save_model.
  2. Run script: python3.5


  1. test result video 1:
  2. test result video 2:

Reference work

  1. keras YOLO v3:
  2. deep_sort:
  3. YOLO v3 deep_sort integration:
  4. optical flow:
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