Run tracking model on a video sequence. Args: video: a torch Tensor that contains a sequence of images, [N, 3, H, W]

Run tracking model on a video sequence. Since some dataset stores video as individual frames and each sequence can be quite large, it

This function is used to initilize the tracking model, typically the starting frame. This is mostly designed for SOT, but can also be

This function is used to initilize SOT with first frame annotation Args: image: this is one image where we used to initi

Compute jaccard score between a box and the anchors.

Returns whether the dataset contains real videos or synthetic returns: bool - True if a video dataset

Returns whether the dataset is a video dataset or an image dataset returns: bool - True if a video dataset

For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops

Puts each data field into a tensor with outer dimension batch size

Puts each data field into a tensor. The tensors are stacked at dim=1 to form the batch

Return the next frame, and increment internal counter. Returns image: Next H x W image. status: True or False depen

create observation hypothesis given a particle and the current observed frame. each row in x contains one particle info. One row of x = [

r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (b

r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bo

r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bo

r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bo

r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args:

r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_ Args:

Runs a function from a child process. Args: local_rank (int): rank of the current process on the current machine. num_proc (i

Most tracking models run frame by frame. This function runs a single forward of the tracking model. Args: image: this is

Run one step of inference from a given search image. Args: img: search image in cv2 format. multiscale_list:

RBF kernel, supporting masked values in the observation Parameters: ----------- x : array (N,D) array of values y : array (N,D) a