{'fraction_non_alphanumeric': 0.016546018614270942, 'fraction_numerical': 0.0, 'mean_word_length': 5.205128205128205, '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}}
Bayesian linear regression for crowd density estimation in aerial images

In this paper, we propose a Bayesian linear regression method for person density estimation in extremely crowded areas in aerial images. The fundamental idea is to learn a mapping function from local features to crowd density. In order to describe the appearances of persons within a crowd in aerial images, local texture features are computed for each small local neighborhood. Then we cast the problem as a linear regression. In order to model the nonlinearity between local features and crowd density, Gaussian basis functions are used and their locations are determined by a k-means clustering. Crowd density can be estimated by Bayesian inference. However, due to the presence of a hyper-prior distribution, variational inference is applied to compute the predictive distribution. Through experiments, the effectiveness of the proposed method for crowd density estimation is demonstrated.