Obstacle detection or tracking moving objects is one of the most interesting topics in computer vision. 
This problem could be solved in two steps:

1, Detecting moving objects in each frame
2, Tracking historical objects with some tracking algorithms

An assignment problem is used to associate the objects detected by detectors and tracked by trackers.

We can found some introduction of this framework here,
https://towardsdatascience.com/computer-vision-for-tracking-8220759eee85

Another example in more detail with matlab code (detecors and trackers may different),
https://www.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html

Here I implemented a highly efficient and scalable C++ framework to combine the state of art 
deep-learning based detectors (Yolo3 demoed here) and correlation filters based trackers 
(KCF, Kalman Filters also implemented). The assignment problem is solved by hungarian algorithm.

Yolo3 is trained for detecting bottles, cans and hands in this demo. It is trained with Keras 
and compiled with tensorflow C++ into a DLL. (YOLO3.DLL under bin folder). CUDA 9.2 is used to
compile the tensorflow C++ library. 

YOLO3.DLL can be compiled with the code under folder detectors and tensorflow C++ library if 
you have tensorflow C++ library compiled.

Kalman filter is fast but less accurate. KCF is accurate but much slower. 
They are implemnted with exactly same interface, so we can easily switch from one to another 
in the project.

OpenCV is used to capture live video frames and used for image preprocessing.

YOLO3.DLL and the model file are too big. They can be downloaded from following link:
https://pan.baidu.com/s/1CPYU2o59vutoq-OJewObRw