YOLOv4 with DeepSORT Object Tracking DEMO| OpenCV Python | Computer Vision |2020

Computer Vision DEMO of vehicle tracking using DeepSORT and YOLOv4 for object detection.

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Hey guys and welcome back, So in the last lecture we created a simple car counting app, In this video I will demonstrate how YOLOv4 can be combined with a powerful tracking model called DeepSORT to track vehicles on a highway.

DeepSORT which was created by Wojke et al in 2017, is a tracking-by detection algorithm. So, what happens is that it only considers information about the current and previous frames to make predictions about the current frame without the need to process the whole video at once. SORT in Deep SORT stands for Simple Online and Realtime Tracking which is an accurate description of the model.

Now tracking assumes that we have an already detected an object of interest. For detection as you may already know is done with YOLOv4. Once we have detected the object, it is assigned an id and is tracked using Deep SORT.

So in this application, we have applied deep sort for tracking of vehicles on a highway for traffic surveillance applications.

►References: https://medium.com/@riteshkanjee/yolov4-superior-faster-more-accurate-object-detection-7e8194bf1872

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