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

Exploring the Applications of Faster R-CNN and Single-Shot Multi-box Detection in a Smart Nursery Domain