C34 | HOG Feature Vector Calculation | Computer Vision | Object Detection | EvODN

We will see how HOG Feature Vectors are extracted.

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This is a part of the course 'Evolution of Object Detection Networks'.
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References:

Credits:

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[Cogneethi]

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[nikronic]

Really clear explanation with numerical example which many tutorials lack of this important feature. Thank you

[muhammetkurt]

I am watching from Turkey and this is the best explanation. I am so appreciate because I found this channel

[varunc]

crisp, clear, to the point with neat diagrams.. thanks

[vasanthc]

Really this video help me to undestand the HOG feature vector claculation in a very simple manner in depth, Simply perfect.

[pallabidas]

Best video on HOG feature descriptors. Thanks sir for sharing ur knowledge.

[JohnSmith-pbcn]

Man, you are really cool in deep explanation of hog. Bravo.

[golden_infinity_harbinger]

Clear, crisp and precise explanation with excellent visualization .
Thank you so much !!!

[Marceloamado]

Thank you for the explanation, you are very good and clear in the way you express yourself.

[yugpn]

Thanks man...You really made consolidated computer vision course that explains all the things with simplicity

[richadhiman]

Very clear excellent video with examples and detail description. Thank you.

[sreeharshaparuchuri]

So, a couple of questions (a great series though!)

At 7:58, why aren't the gradient lines STRICTLY along the x or y direction (depending on the part of the arrow), why is there a slight tilt?

At 5:34, what's the point of concatenating 4 9x1 feature vectors? An 8x8 patch is already a huge patch from which one can calculate the histogram. Also, this method of concatenation results in overlapping between windows (maybe this is alright as we would like to detect subtle changes) but if we didn't concatenate feature vectors, our image would be represented by only 8 * 16 * 9 = 1152 numbers, thus, saving a lot of compute power.

I hope that i've asked my questions clearly enough.

[thaimeuu]

thanks bro, now I think this field is so fun

[mailoisback]

Thanks for the video! At 08:15 you say that when we transition from white to black, the change in gradient magnitude is along the x-direction. But isn't it changing in y-direction? I think that in the hog visualization we see the orientations of edges, not the gradients (which are perpendicular to edges).

[chandraprakash]

1:35 Why grad direction tan-1(30/50) and not tan-1(50/30) ? It should be y/x right ?

[Banditpundit]

Nice and clear explanation, thank you!!

[janosbagi]

Very nice explanation. Thank you very much !

[kinsung]

For the 8*8 matrix in each cell/grid, you calculate the gradient magnitude and gradient direction. There will be missing values at the edge of the calculation. What numbers to fill in for the pixels value at the edge?

[somdubey]

Hi. Thanks for such an informative video. I have a small question, shouldn't the Gradient direction be tan^(-1)(y/x), or tan^(-1)(50/30) instead of tan^(-1)(30/50) !!

[hemantpurswaniAI]

grad direction will be tan inverse (50/30)?