Person classification from aerial imagery using local convolutional neural network features

Abstract

ABSTRACTThe need for search and rescue is not one that will go away. Extending beyond the concerns of hikers, natural disasters have brought the necessity for search and rescue in the heart of civilization. The advent of new technologies, such as unmanned aerial vehicles, can help decrease the cost of search and rescue operations and increase survival rates by finding the lost individuals in a faster manner. Visual object recognition is key to successful drone application for assisting search and rescue activities, and it is critical to develop a fully autonomous system. In recent years, deep convolutional neural networks have proven themselves as a powerful class of models and have become de facto a standard in a computer vision community. Moreover, a number of studies have shown that intermediate activations extracted from a deep convolutional neural network, pre-trained on large image dataset, can be adopted as universal image representation and transferred to other image classification tasks, leading to striking performances. In this paper, we investigate the effectiveness of different layer activations on the performance of convolutional features for binary person classification from UAV imagery, using various deep network architectures. Experiments have demonstrated that convolutional network generated features can deliver extremely competitive results, comparable to human-level performance. Furthermore, they reveal that in said context fully connected layer activations generalize well and are more expressive than the mid- or higher-level layers activations. Our work provides guidance for transferring pre-trained deep convolutional neural networks to address remote person classification tasks.