  In this project, we adapt high-performing CNN architectures to differentiate
between scenes with and without abandoned luggage. Using frames from two video
datasets, we compare the results of training different architectures on each
dataset as well as on combining the datasets. We additionally use network
visualization techniques to gain insight into what the neural network sees, and
the basis of the classification decision. We intend that our results benefit
further work in applying CNNs in surveillance and security-related tasks.
