Estimate object size in the wild without camera calibration or handcrafted features with PMODE
Estimate object size in the wild without camera calibration or handcrafted features with PMODE
PMODE: Prototypical Mask based Object Dimension Estimation
arXiv paper abstract https://arxiv.org/abs/2212.13281
arXiv PDF paper https://arxiv.org/pdf/2212.13281.pdf
… propose a method and deep learning architecture to estimate the dimensions of a quadrilateral object of interest in videos using a monocular camera.
… does not use camera calibration or handcrafted geometric features; however, features are learned with the help of coefficients of a segmentation neural network during the training process.
A real-time instance segmentation-based Deep Neural Network with a ResNet50 backbone is employed, giving the object’s prototype mask and thus provides a region of interest to regress its dimensions.
The instance segmentation network is trained to look at only the nearest object of interest.
The regression is performed using an MLP head which looks only at the mask coefficients of the bounding box detector head and the prototype segmentation mask.
… trained the system with three different random cameras achieving 22% MAPE for the test dataset for the dimension estimation
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