  The ultimate goal of a baby detection task concerns detecting the presence of
a baby and other objects in a sequence of 2D images, tracking them and
understanding the semantic contents of the scene. Recent advances in deep
learning and computer vision offer various powerful tools in general object
detection and can be applied to a baby detection task. In this paper, the
Faster Region-based Convolutional Neural Network and the Single-Shot Multi-Box
Detection approaches are explored. They are the two state-of-the-art object
detectors based on the region proposal tactic and the multi-box tactic. The
presence of a baby in the scene obtained from these detectors, tested using
different pre-trained models, are discussed. This study is important since the
behaviors of these detectors in a baby detection task using different
pre-trained models are still not well understood. This exploratory study
reveals many useful insights into the applications of these object detectors in
the smart nursery domain.
