{'fraction_non_alphanumeric': 0.017276422764227643, 'fraction_numerical': 0.017276422764227643, 'mean_word_length': 4.863095238095238, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SURF-LSTM: A Descriptor Enhanced Recurrent Neural Network For Indoor Localization

We propose SURF-LSTM, a low complexity deep architecture to learn image absolute pose (position and orientation) in indoor environments using SURF descriptors and recurrent neural networks. Given the strongest SURF features descriptors of an input image, we use 2 layers of bidirectional long short term memory (LSTM) to model the sequential relation between them to learn the 6 degrees of freedom absolute pose in an arbitrary reference frame. In addition to achieving competitive performance compared to existing image localization methods, our system can be trained in less than 10 minutes instead of hours by the state of the art. It requires as small as 0.0128 MB to save the image frame rather than 0.08 MB compared to other methods that use the cropped images and the weights file needs 1.5 MB of storage compared to 100 MB of other methods which leads to significant time and space efficiency.