Begin:
Identification contd.
> Tried using the histogram of a road, to extract only the road from a traffic image.
Steps were like:
> Get the HSV of both the template and original image.
> Get the histogram of the template road.
> Use the back projection method of open CV to extract only those parts that match with the histograms of the template.
> Next imp step is to optimize the match, using kernel estimation and thresholding.
a. Kernel estimation, acc to my understanding estimates the density/intensity of the given pixel using a prescribed filter (Ellipse of a given size, circle of a given size, etc ..)
b. Thresholding tells the system to consider all the values below a threshold as black and above that threshold as white.
The above 2 filtering steps is done for the mask to improve the mask that selects the road :
Final step is to do merge (bitwise and ) the mask with the original image to get the below image on the left.
TakeAways:
1. Using histogram of the template ROI (region of interest) to filter out specific regions from the given image.
2. Best use case that I could think of is to track a given object in real time.
3. Next, I would want to be able to custom select an area in real time, and start tracking parts of the image that matches with the given bounding box.
To be contd..
Identification contd.
> Tried using the histogram of a road, to extract only the road from a traffic image.
Steps were like:
> Get the HSV of both the template and original image.
> Get the histogram of the template road.
> Use the back projection method of open CV to extract only those parts that match with the histograms of the template.
> Next imp step is to optimize the match, using kernel estimation and thresholding.
a. Kernel estimation, acc to my understanding estimates the density/intensity of the given pixel using a prescribed filter (Ellipse of a given size, circle of a given size, etc ..)
b. Thresholding tells the system to consider all the values below a threshold as black and above that threshold as white.
The above 2 filtering steps is done for the mask to improve the mask that selects the road :
Final step is to do merge (bitwise and ) the mask with the original image to get the below image on the left.
TakeAways:
1. Using histogram of the template ROI (region of interest) to filter out specific regions from the given image.
2. Best use case that I could think of is to track a given object in real time.
3. Next, I would want to be able to custom select an area in real time, and start tracking parts of the image that matches with the given bounding box.
To be contd..
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