{'fraction_non_alphanumeric': 0.014084507042253521, 'fraction_numerical': 0.0, 'mean_word_length': 4.98019801980198, '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}}
An adaptive multi-layer particle filter for tracking of traffic participants in a roundabout

This paper describes an extension of a standard particle filter for application on multiple object tracking using a single probabilistic density function, at which the method is based on image sensor values coming from a monocular camera. The idea of the new approach is that each cell of the tracking area is associated with a so called layer, by using a new introduced layer distribution. The automatic adaption of the layer values occurs by the weights of the particles which are located in the cells of the tracking area. Depending on the layers, which are associated with the single particle positions, the particles get different areal extents. So, the posterior density is approximated using particles which have different areal extents. The principle of multiple layers is also used for finding new appearing objects via a reinitialization step. On basis of such a multi-layer particle filter, the tracking quality and robustness can be increased compared to the conventional method, especially when the measurement quality is low. The good performance is shown using a real traffic scene in a roundabout.