Improve crowd counting by varying the weight with Mask Focal Loss
Improve crowd counting by varying the weight with Mask Focal Loss
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks
arXiv paper abstract https://arxiv.org/abs/2212.11542
arXiv PDF paper https://arxiv.org/pdf/2212.11542.pdf
As a fundamental computer vision task, crowd counting predicts the number of pedestrians in a scene, which plays an important role in risk perception and early warning, traffic control and scene statistical analysis.
Currently, deep learning based head detection is a promising method for crowd counting.
... propose a novel loss function, called Mask Focal Loss (MFL), to redefine the loss contributions according to the situ value of the heatmap with a Gaussian kernel.
MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths.
Meanwhile, for better evaluation and comparison, a new synthetic dataset GTA_Head is built, including 35 sequences, 5096 images and 1732043 head labels with bounding boxes.
... show the overwhelming performance and demonstrate that ... proposed MFL framework is applicable to all of the canonical detectors and to various datasets with different annotation patterns ...
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