Title: Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields,
Abstract: Overhead depth map measurements capture sufficient amount of information to
enable human experts to track pedestrians accurately. However, fully automating
this process using image analysis algorithms can be challenging. Even though
hand-crafted image analysis algorithms are successful in many common cases,
they fail frequently when there are complex interactions of multiple objects in
the image. Many of the assumptions underpinning the hand-crafted solutions do
not hold in these cases and the multitude of exceptions are hard to model
precisely. Deep Learning (DL) algorithms, on the other hand, do not require
hand crafted solutions and are the current state-of-the-art in object
localization in images. However, they require exceeding amount of annotations
to produce successful models. In the case of object localization these
annotations are difficult and time consuming to produce. In this work we
present an approach for developing pedestrian localization models using DL
algorithms with efficient weak supervision from an expert. We circumvent the
need for annotation of large corpus of data by annotating only small amount of
patches and relying on synthetic data augmentation as a vehicle for injecting
expert knowledge in the model training. This approach of weak supervision
through expert selection of representative patches, suitable transformations
and synthetic data augmentations enables us to successfully develop DL models
for pedestrian localization efficiently.