I am a beginner and have just started studying machine learning and neural networks and have just understood the very basics of this vast and interesting domain.
From my basic knowledge, I know that a model/classifier can be used to Classify an image as something. But I was curious if there is a way to detect multiple instances of the same object and count the same.
Basically I wanted to calculate the density of traffic at a red light to dynamically control the flow of traffic, so I was curious if there was a way to detect multiple cars and count the number of cars at a red light by training the ConvNet on Images of Cars (and if there is a way to implement the same using tensor-flow)
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2 Answers
You might consider using an off the shelf object detector, e.g., the Tensorflow Object Detection API (github.com/tensorflow/models/tree/master/object_detection) to first detect cars, and then count them.
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CNN is one branch of the machine learning. It can be trained to classify different cars as one class, just like many other technologies applied in machine learning.
My understanding of your question is: you want to count the number of cars at the red light and make decision of the traffic dynamically. So I would seperate your question into two part
- Count the number of cars
- Optimize the traffic flow
For the question 1 which you are actually interested in I would suggest you to have a look at: Counting the number of vehicles from an image with machine learning
I hope this can be helpful
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Yirui Jiang
This maybe also helpful stackoverflow.com/questions/42375680/…