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Update: I misread the question; It seems like OP is more interested in pose tracking. So, I'll have to point OP to papers on that, like this one. Using multiple frames becomes especially important when there are multiple people in the frame, and it's desired to track which pose belongs to which person. For more papers on pose tracking, look here. Reading ...


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I don't think there is a great difference between pose estimation in pictures and in videos. Do you know MediaPipe? https://google.github.io/mediapipe/ MediaPipe does perform an tracking from the keypoints through time. So the temporal information is used. Apart from that it is the same problem as the estimation from keypoints in static images.


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You could just go with any fully convolutional network that has the same output resolution as the image input resolution (e.g. from semantic segmentation) as your backbone. Then Solution 1: You could adapt the last layer to have 4 feature maps where the first 2 maps represent the proability of existance or non-existance of point 1. And the last 2 maps ...


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It of course depends on the detection model that is used. But in your case I think you relate to a Faster-RCNN type architecture for bounding box detection. In this case only the relative position to an anchor is regressed, that is correct. Of course the regression "values" isolated have no information where they are absolute in the image since the ...


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The classification head works as follows. After the stack of BiFPN we have a feature map of size B x C x H x W. For EfficientDet H and W are 1/8 of the input image size. Then for each pixel in this feature map one applies one convolution to get the bounding boxes. The model predicts n_anchors - rescaled and shifted versions of reference boxes. The number of ...


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The estimation of optical flow by the Horn-Schunk method can be written in matrix form as (for the sake of clarity, assume no regularization, that is, $\alpha = 0$) $$ \left( \begin{array}{ll} I_x^2 & I_xI_y \\ I_xI_y & I_y^2 \\ \end{array} \right) \left( \begin{array}{ll} u \\ v \end{array} \right) = \left( \begin{array}{ll} -I_x I_t \\ -I_y I_t \...


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I explain in this answer what a projective transformation (aka projectivity or homography) is. It's a function $h$ of the form $$h: \mathbb{P}^2 \rightarrow \mathbb{P}^2,$$ where $\mathbb{P}^2$ is a projective space, so, essentially, a 3-dimensional Euclidean space of homogenous vectors. You can also represent a homography as a $3 \times 3$ matrix $\mathbf{H}...


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This question is studied in a recent research - Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels. It is quite strange, that community has paid so little attention to this issue. They have relabeled the original ImageNet with the help of crops and changed the task to the multilabel. This strategy turned out to be quite ...


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