{'fraction_non_alphanumeric': 0.0297029702970297, 'fraction_numerical': 0.004243281471004243, 'mean_word_length': 5.706161137440758, '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}}
Multi-Label Crowd Size And Location Recognition From Images

Excessive crowd concentration or movement may have devastating consequences. Technology-assisted crowd size monitoring systems can provide early warnings and apply preemptive safety measures. Existing automatic head-counting techniques are computationally complex, time-consuming, and rely on high image resolution. The study proposes a rapid crowd-size assessment from images that classifies crowd size in terms of three qualitative labels (large, small, and no crowd). In addition, it gives a simultaneous recognition of the crowd location (airport, mall, train station, and beach). At the first stage, the classification process applies convolutional neural networks (CNNs) trained to classify crowd size and location. At the second stage, a shallow neural network (NN) meta classifier is trained on the probability vectors generated by the first-stage CNNs to give further refinement of the classification outcomes. Validation experiments show that the second stage improves upon the first stage classification results. A comparison with baseline classification techniques using five different CNN architectures (Alex Net, ResNet50, ResNet101, InceptionV3, and Inception-ResNet) is included. The effect of the CNN complexity and the application of a single versus multiple first-stage classifiers on the overall classification results are discussed.