  Despite recent advances in video-based action recognition and robust
spatio-temporal modeling, most of the proposed approaches rely on the abundance
of computational resources to afford running huge and computation-intensive
convolutional or transformer-based neural networks to obtain satisfactory
results. This limits the deployment of such models on edge devices with limited
power and computing resources. In this work we investigate an important smart
home application, video based delivery detection, and present a simple and
lightweight pipeline for this task that can run on resource-constrained
doorbell cameras. Our method relies on motion cues to generate a set of coarse
activity proposals followed by their classification with a mobile-friendly
3DCNN network. To train we design a novel semi-supervised attention module that
helps the network to learn robust spatio-temporal features and adopt an
evidence-based optimization objective that allows for quantifying the
uncertainty of predictions made by the network. Experimental results on our
curated delivery dataset shows the significant effectiveness of our pipeline
and highlights the benefits of our training phase novelties to achieve free and
considerable inference-time performance gains.
