DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE1 Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as realworld videos acquired through multiple mobile platforms. The final results show an improvement over the existing stateoftheart visual place recognition systems.1Supplementary material including code and the videos of the different experiments are available at https:sites.google.comeng.ucsd.edudeduce.

Scene recognition: A comprehensive survey Abstract   With the success of deep learning in the field of computer vision, object recognition has made important breakthroughs, and its recognition accuracy has been drastically improved. However, the performance of scene recognition is still not sufficient to some extent because of complex configurations. Over the past several years, scene recognition algorithms have undergone important evolution as a result of the development of machine learning and Deep Convolutional Neural Networks (DCNN). This paper reviews many of the most popular and effective approaches to scene recognition, which is expected to create benefits for future research and practical applications. We seek to establish relationships among different algorithms and determine the critical components that lead to remarkable performance. Through the analysis of some representative schemes, motivation and insights are identified, which will help to facilitate the design of better recognition architectures. In addition, current available scene datasets and benchmarks are presented for evaluation and comparison. Finally, potential problems and promising directions are highlighted.