Detection of valuable left-behind items in vehicle cabins

Abstract

We propose a method for detecting valuable left-behind items in vehicle cabins which uses a single overhead camera. An additional sub-network is incorporated into the Faster R-CNN framework in order to allow it to estimate item value based on visual properties, as well as to perform detection. A loss function which contains a user-specified minimum-value threshold is introduced, which enables warnings to be given if a detected item is above this threshold. As a significant amount of real data is time consuming to collect on the scale necessary for (deep) learning-based methods, an ImageNet model is first retrained on synthetic data to adapt it to our environment, before training on some real data. The effectiveness of this detection and validation approach is demonstrated by integrating additional valuation subnetworks into two convolutional neural network detection architectures.