amazing video. But if we want to implement this on our own dataset, how could we annotate this raw images? So the training input is an rgb image and a json file with the keypoints and then apply cnn regression on that?
Thanks @prajwal. Openpose have one integration with the opencv. You can check it out there. github.com/opencv/opencv/blob/master/samples/dnn/openpose.py
Thanks for the great video! Question: at 5:35 why are there 38 different pairs in total? Wouldn't the number of different pairs depend on the number of humans in the scene?
@@gurvindersingh-tz1jk Hey thank you so much for your clarification! That makes more sense. Though could you please tell me how did we get 38 different paths? I understand that we have 18 joints and that we're trying to combine 2 joints to get a legit pair, but where did the number 38 come from? Are we calculating each pair twice (eg: neck to torso and torso to neck?) Thank you!
Thanks man, really great explanation
Thanks for the great explanation!
very informative, thanks
I suggest you add the author and the paper link otherwise which this is a infringement of copyrights! Thanks for the video! Go deeper in the next case
Thank you.
Thank you for the comment
Good video! Thank you!
Thanks Alessandro..
amazing video. But if we want to implement this on our own dataset, how could we annotate this raw images? So the training input is an rgb image and a json file with the keypoints and then apply cnn regression on that?
Wonderful explanation. Could you share the code/implementation please?
Thanks @prajwal. Openpose have one integration with the opencv. You can check it out there.
github.com/opencv/opencv/blob/master/samples/dnn/openpose.py
Angel (farishta) in disguise
People like you will definitely postpone Kalki's reanimation
Can we find distance between two node points for abnormal gait.? please make a video on that.
Thanks for the great video! Question: at 5:35 why are there 38 different pairs in total? Wouldn't the number of different pairs depend on the number of humans in the scene?
38 different pairs are for each person, the total number of pairs equal to 38*n, where n is the number of humans.
@@gurvindersingh-tz1jk Hey thank you so much for your clarification! That makes more sense. Though could you please tell me how did we get 38 different paths? I understand that we have 18 joints and that we're trying to combine 2 joints to get a legit pair, but where did the number 38 come from? Are we calculating each pair twice (eg: neck to torso and torso to neck?) Thank you!
such a clear explanation. well done, paaji!
hi, thanks for your videos.... i have a question please, i want to know how encodage using part of affinity is done.
okay I will make the video on how encoding using part of affinity done in details
Thanks for the video, can you make a video on PoseNet vs OpenPose .
Currently I'm not working on Pose Estimation Problem but in future i will try
@@Rednivrug sure, thank you
Sir i need to generate or locate auscultation points
Thank you for your helpful video , could you give me the presentation slides of this video please
Here is the presentation slides.
www.linkedin.com/posts/rednivrug_open-pose-presentation-activity-6580330239241613312-vkm5
@@Rednivrug Thank you !
Ya .This is good.Can u send me the code for this.
I have a question. Why VGG-19 is using there? For detecting person or what?
Vgg19 is kond of backbone which is used to extract the features of the image.
@@Rednivrug Ok. Thanks for your answer!
where can i get the code