{'fraction_non_alphanumeric': 0.017857142857142856, 'fraction_numerical': 0.0, 'mean_word_length': 5.3229571984435795, '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}}
Resource constrained particle filtering for real-time multi-target tracking in sensor networks

This paper introduces methods for allocating computational resources to track multiple targets in a sensor network of cameras, with the aim of maximizing tracking accuracy, an aspect of information quality (IQ). Particle filters are used where an independent filter is created for each target track. The particle filter's strength to track complex probability models comes at the cost of a higher demand for computational resources. As there is a direct correlation between the number of particles used and the computational costs, a means to meet the computational resource constraints is to limit the total number of particles. This motivates an online distribution of available particles to available tracks to maximise tracking accuracy. Restricting the number of particles may result in ineffective particle filters that diverge from the true target position. This issue has not been addressed in any known work of allocating particles. Our solution is a particle allocation scheme that allocates available particles to existing tracks based on both filter uncertainty and the effectiveness of the particle sample set. A method to allocate assigned particles in a track to overcome suspected divergence is also applied. Experimental results, from a multi-target tracking simulation, show that given the same number of particles to allocate, our scheme outperforms two other methods, which are (a) an equal (fixed) allocation of particles to each filter and (b) an allocation of particles solely based on filter uncertainty.