Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Parameter Estimation of Social Forces in Crowd Dynamics Models via a Probabilistic Method

Focusing on a specific crowd dynamics situation, including real life experiments and measurements, our paper targets a twofold aim: (1) we present a Bayesian probabilistic method to estimate the value and the uncertainty (in the form of a probability density function) of parameters in crowd dynamic models from the experimental data; and (2) we introduce a fitness measure for the models to classify a couple of model structures (forces) according to their fitness to the experimental data, preparing the stage for a more general model-selection and validation strategy inspired by probabilistic data analysis. Finally, we review the essential aspects of our experimental setup and measurement technique.Comment: 20 pages, 9 figure

Design And Implementation Of Human Crowd Density Estimation System With Energy Harvesting In Wireless Sensor Network Platform

Kepadatan yang tinggi dalam khalayak ramai boleh menjadi berbahaya kerana wujudnya potensi untuk pergerakan sekumpulan manusia secara tiba-tiba yang menyebabkan rempuhan dalam kes kecemasan. Untuk mengurangkan kecederaan mahupun kehilangan nyawa dalam kemalangan yang berkaitan dengan isu kepadatan manusia, sistem pengawasan kepadatan manusia berdasarkan frekuensi radio telah dibangunkan sebagai satu alat keselamatan. Sistem yang didapati pada masa kini mempunyai keupayaan pengawasan yang terhad; saiz pengawasan khalayak yang rendah, jarak pengesanan yang rendah, keperluan bilangan alat komunikasi yang tinggi dan jangka hayat operasi yang terhad. Faktor-faktor ini memberi kesan secara langsung kepada unsur praktikal dan ketepatan sistem penganggaran kepadatan manusia tersebut. Untuk mengurangkan kelemahan keupayaan pengawasan, satu sistem untuk mengesan kepadatan khalayak diusulkan berdasarkan kepada teknologi ZigBee dan rangkaian pengesan tanpa wayar yang meningkatkan jarak pengesanan khalayak kepada 30 m dengan hanya satu nod diperlukan setiap 37.5 m2. Hal ini dicapai tanpa mengurangkan bilangan khalayak (50 orang) yang boleh dikesan oleh sistem. Untuk menambahbaik ketepatan anggaran, kesan khalayak terhadap isyarat diselidik menggunakan kaedah statistik ‘One-way Analysis of Variance’ dan ‘Design of Experiments’. Hasil dapatan mengesahkan saiz khalayak memberi kesan yang paling besar terhadap kelemahan isyarat. Untuk interaksi di antara sifat-sifat khalayak, didapati saiz khalayak bersama bilangan alat penerima dan bentuk khalayak bersama bilangan alat penerima memberi kesan signifikan terhadap kekuatan isyarat. Faktor-faktor ini kemudian dimasukkan ke dalam algoritma H-CDE yang diusulkan. Algoritma pengesanan khayalak ini dan pengelasannya menunjukkan purata sebanyak 71.2 peratus ketepatan dalam mengenalpasti tahap kepadatan khalayak yang juga dapatan terbaik berbanding algoritma lain. Untuk mengatasi masalah kuasa yang terhad, mekanisma tuaian tenaga solar diperkenalkan ke dalam sistem H-CDE untuk memanjangkan jangka hayat operasi pengawasan. Kajian menunjukkan mekanisma tuaian tenaga ini mampu untuk memanjangkan operasi sistem pengawasan secara berterusan jika sistem ini mendapat paling kurang 5 hingga 6 jam pendedahan kepada sinaran matahari setiap 33 jam kitaran. Sumbangan kajian ini ialah pada penambahbaikan sistem berdasarkan teknologi frekuensi radio untuk mengesan kepadatan khalayak, penambahbaikan pada ketepatan penganggaran kepadatan khalayak yang didokongi oleh analisis statistik dan lanjutan operasi sistem melalui mekanisma tuaian tenaga. __________________________________________________________________________________ A crowd with high density can be dangerous due to the potential of a sudden surge of large moving bodies causing stampede in cases of emergencies. To mitigate casualties in crowd-related disaster, radio frequency-based crowd density estimation and monitoring system is being developed as a safety tool. Current systems have limited monitoring capabilities; low size of crowd monitored, low detection range, high number of transceivers required and finite operational lifetime. These factors directly influence the practicality and prediction accuracy of the system. To mitigate the limited sensing capability, a human crowd density estimation (H-CDE) system based on ZigBee and wireless sensor network technology is proposed that increases the crowd detection range to 30 m with only one transmission node required every 37.5 m2. This is achieved without sacrificing the amount of crowd detectable by the system (50 people). To improve the estimation accuracy, the effect of crowd on signal propagation is investigated using One-way Analysis of Variance and Design of Experiments statistical methods. The results verified that the crowd size significantly affects the signal attenuation. In the interactions between the crowd properties, crowd size * number of receiver and crowd pattern * number of receiver were found to significantly affect signal propagation. These factors are then integrated into the proposed H-CDE algorithm. The H-CDE algorithm and its crowd classification yielded an average of 71.2 % accuracy in identifying the level of crowd density, which is the best compared to other algorithms found in the literature. To solve the finite power problem, a solar energy harvesting mechanism is introduced into the H-CDE system to extend the operation of the monitoring system. It is demonstrated that the proposed energy harvesting mechanism could operate perpetually, given that the system is exposed to good sunlight at least for 5 to 6 hours in every 33-hour cycle. The contribution of the research is on the improved RF-based crowd density detection system, improved crowd estimation accuracy which is backed by statistical analysis and extension of its operations through the energy harvesting mechanism

Online real-time crowd behavior detection in video sequences

Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach