How can I detect partially obscured objects using Python?
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I'm building a computer vision application using Python (OpenCV, keras-retinanet, tensorflow) which requires detecting an object and then counting how many objects are behind that front object. So, objects are often overlapping.
For example: How many are in this queue?
Here, the aim is to detect the person in the front (foreground) of the queue, and then detect the amount of people behind the front person, despite those behind being obscured.
I have built an object recognition model using keras-retinanet to find the object in the foreground, but now I need to count how many are behind that object. My training data is thousands of images of objects ins straight lines, similar to the image linked above.
However, I have been unable to find a viable way to count the objects behind the front object when they are partially obscured (the rest of the people in the queue).
So far I have tried counting edges (using auto-thresholding), lowering the confidence threshold of my object detection model, and training a new object detection model using only obscured objects as training. These methods have all had very low accuracy.
Do you have any advice or direction on how I can attack this problem?
python keras computer-vision image-recognition opencv
asked 6 mins ago
Mark PresserMark Presser
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I'm building a computer vision application using Python (OpenCV, keras-retinanet, tensorflow) which requires detecting an object and then counting how many objects are behind that front object. So, objects are often overlapping.
For example: How many are in this queue?
Here, the aim is to detect the person in the front (foreground) of the queue, and then detect the amount of people behind the front person, despite those behind being obscured.
I have built an object recognition model using keras-retinanet to find the object in the foreground, but now I need to count how many are behind that object. My training data is thousands of images of objects ins straight lines, similar to the image linked above.
However, I have been unable to find a viable way to count the objects behind the front object when they are partially obscured (the rest of the people in the queue).
So far I have tried counting edges (using auto-thresholding), lowering the confidence threshold of my object detection model, and training a new object detection model using only obscured objects as training. These methods have all had very low accuracy.
Do you have any advice or direction on how I can attack this problem?
python keras computer-vision image-recognition opencv
asked 6 mins ago
Mark PresserMark Presser
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm building a computer vision application using Python (OpenCV, keras-retinanet, tensorflow) which requires detecting an object and then counting how many objects are behind that front object. So, objects are often overlapping.
For example: How many are in this queue?
Here, the aim is to detect the person in the front (foreground) of the queue, and then detect the amount of people behind the front person, despite those behind being obscured.
I have built an object recognition model using keras-retinanet to find the object in the foreground, but now I need to count how many are behind that object. My training data is thousands of images of objects ins straight lines, similar to the image linked above.
However, I have been unable to find a viable way to count the objects behind the front object when they are partially obscured (the rest of the people in the queue).
So far I have tried counting edges (using auto-thresholding), lowering the confidence threshold of my object detection model, and training a new object detection model using only obscured objects as training. These methods have all had very low accuracy.
Do you have any advice or direction on how I can attack this problem?
python keras computer-vision image-recognition opencv
asked 6 mins ago
Mark PresserMark Presser
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm building a computer vision application using Python (OpenCV, keras-retinanet, tensorflow) which requires detecting an object and then counting how many objects are behind that front object. So, objects are often overlapping.
For example: How many are in this queue?
Here, the aim is to detect the person in the front (foreground) of the queue, and then detect the amount of people behind the front person, despite those behind being obscured.
I have built an object recognition model using keras-retinanet to find the object in the foreground, but now I need to count how many are behind that object. My training data is thousands of images of objects ins straight lines, similar to the image linked above.
However, I have been unable to find a viable way to count the objects behind the front object when they are partially obscured (the rest of the people in the queue).
So far I have tried counting edges (using auto-thresholding), lowering the confidence threshold of my object detection model, and training a new object detection model using only obscured objects as training. These methods have all had very low accuracy.
Do you have any advice or direction on how I can attack this problem?
python keras computer-vision image-recognition opencv
python keras computer-vision image-recognition opencv
asked 6 mins ago
Mark PresserMark Presser
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
Mark PresserMark Presser
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
Mark PresserMark Presser
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
Mark PresserMark Presser
1
asked 6 mins ago
Mark PresserMark Presser
1
1
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mark Presser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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