A graphical simulator for modeling complex crowd behaviors

Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts - video recordings - from the real-world

Area & Perimeter Surveillance in SAFEST using Sensors and the Internet of Things

International audienceSAFEST is a project aiming to provide a comprehensive solution to ensure the safety and security of the general public and critical infrastructures. The approach of the project is to design a lightweight, distributed system using heterogeneous, networked sensors, able to aggregate the input of a wide variety of signals (e.g. camera, PIR, radar, magnetic, seismic, acoustic). The project aims for a proof-of-concept demonstration focusing on a concrete scenario: crowd monitoring, area and perimeter surveillance in an airport, realized with a prototype of the system, which must be deployable and foldable overnight, and leverage autoconfiguration based on wireless communications and Internet of Things. This paper reviews the progress towards reaching this goal, which is planned for 2015

Análise de multidões usando coerência de vizinhança local

Large numbers of crowd analysis methods using computer vision have been developed in the past years. This dissertation presents an approach to explore characteristics inherent to human crowds – proxemics, and neighborhood relationship – with the purpose of extracting crowd features and using them for crowd flow estimation and anomaly detection and localization. Given the optical flow produced by any method, the proposed approach compares the similarity of each flow vector and its neighborhood using the Mahalanobis distance, which can be obtained in an efficient manner using integral images. This similarity value is then used either to filter the original optical flow or to extract features that describe the crowd behavior in different resolutions, depending on the radius of the personal space selected in the analysis. To show that the extracted features are indeed relevant, we tested several classifiers in the context of abnormality detection. More precisely, we used Recurrent Neural Networks, Dense Neural Networks, Support Vector Machines, Random Forest and Extremely Random Trees. The two developed approaches (crowd flow estimation and abnormality detection) were tested on publicly available datasets involving human crowded scenarios and compared with state-of-the-art methods.Métodos para análise de ambientes de multidões são amplamente desenvolvidos na área de visão computacional. Esta tese apresenta uma abordagem para explorar características inerentes às multidões humanas - comunicação proxêmica e relações de vizinhança - para extrair características de multidões e usá-las para estimativa de fluxo de multidões e detecção e localização de anomalias. Dado o fluxo óptico produzido por qualquer método, a abordagem proposta compara a similaridade de cada vetor de fluxo e sua vizinhança usando a distância de Mahalanobis, que pode ser obtida de maneira eficiente usando imagens integrais. Esse valor de similaridade é então utilizado para filtrar o fluxo óptico original ou para extrair informações que descrevem o comportamento da multidão em diferentes resoluções, dependendo do raio do espaço pessoal selecionado na análise. Para mostrar que as características são realmente relevantes, testamos vários classificadores no contexto da detecção de anormalidades. Mais precisamente, usamos redes neurais recorrentes, redes neurais densas, máquinas de vetores de suporte, floresta aleatória e árvores extremamente aleatórias. As duas abordagens desenvolvidas (estimativa do fluxo de multidões e detecção de anormalidades) foram testadas em conjuntos de dados públicos, envolvendo cenários de multidões humanas e comparados com métodos estado-da-arte

ANALISIS DAN DETEKSI GERAKAN KEPANIKAN MENGGUNAKAN METODE FRAME DIFFERENCE PADA SISTEM MONITORING RUMAH

Kepanikan, panik, gangguan panik atau serangan panik adalah semacam kecemasan dengan ciri diserang rasa takut yang luar biasa selama beberapa menit, timbulnya perasaan bahwa ada sesuatu akan terjadi, atau adanya ketidakmampuan untuk mengendalikan diri sekalipun sebenarnya tidak ada sesuatu yang buruk yang benar-benar terjadi. Seseorang dapat merasakan sensasi fisik yang kuat selama serangan panik berlangsung. Sensasi fisik itu mungkin terasa seperti berlari kencang atau mengalami serangan jantung. Sistem pengawasan saat ini menggunakan teknologi kamera. Suatu kebutuhan yang berkembang untuk pengawasan video yang lebih cerdas dari ruang pribadi atau publik menggunakan sistem penglihatan cerdas yang dapat membedakan apa yang secara semantik penting dalam arah pengamat manusia sebagai perilaku panik dan perilaku normal. Pada tugas akhir kali ini dirancang sistem untuk mendeteksi gerakan kepanikan berdasarkan kecepatan langkah kaki manusia. Metode yang digunakan untuk proses pengolahan citra dalam sistem adalah frame difference. Sistem ini dengan metode frame difference berhasil melakukan deteksi kepanikan berdasarkan kecepatan manusia pada sudut depresi 25o dengan menggunakan pencahayaan 100-250 Lux pada posisi gerakan lurus mendapatkan akurasi 71.43% dan gerakan tidak lurus mendapatkan akurasi 85.71%. Kata Kunci : Panic Detection, Frame Difference, Object Detectio