Computer Vision for tracking

Author Topic: Computer Vision for tracking  (Read 280 times)

Offline s.arman

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Computer Vision for tracking
« on: April 21, 2019, 02:22:57 AM »
In Computer Vision, one of the most interesting area of research is obstacle detection using Deep Neural Networks. A lot of papers went out, all achieving SOTA (State of the Art) in detecting obstacles with a really high accuracy. The goal of these algorithms is to predict a list of bounding boxes from an input image. Machine Learning has evolved really well into localising and classifying obstacles in real-time in an image. However, none of these algorithm include the notion of time and continuity. When detecting an obstacle, these algorithms assume it’s a new obstacle every time.

I won’t go into the details of the algorithm here, but you can have a look at this video from Siraj Raval that explains it very well.
The output of the algorithm is a list of bounding box, in format [class, x, y, w, h, confidence]. The class is an id related to a number in a txt file (0 for car , 1 for pedestrian, …). x, y, w and h represent the parameters of the bounding box. x and y are the coordinates of the center while w and h are its size (width and height). The confidence is a number expressed in %.

source:https://towardsdatascience.com/computer-vision-for-tracking-8220759eee85?fbclid=IwAR2ksoHRNrwL6r-MzKAvvPycCmPuJqDPb_2MpYZutzjcnxLNkOFDQrf5Smo

Offline lamisha

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Re: Computer Vision for tracking
« Reply #1 on: July 10, 2019, 09:36:43 AM »
thanks for sharing