Segmentation and Counting of People Through Collaborative Augmented Environment

People counting system have wide potential application including video surveillance and public resources management. Also with rapid development of economic society, crowd flowing in varies public places and facility is more and more frequent. Effectively managing and controlling crowd in public places become an important issue. People counting system based on this kind of demand arises, which can be used in commercial domain such as market survey, traffic management as well as architectural design domain. For example suppose there is a crowd gathering at specific place then it indicates an unusual situation and second one if counting of people is done in shopping mall then it provides valuable information for optimizing trading hours, as well as evaluating the attractiveness of some shopping areas

Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

Аналіз натовпу людей із застосуванням методів комп’ютерного зору

Розглянуто три важливі проблеми аналізу натовпу: підрахунок людей/оцінка щільності, відстежування в сценах з натовпом людей та розуміння поведінки натовпу. Автоматизоване оцінювання і підрахунок щільності натовпу – актуальна і важлива тема в аналізі натовпу. В статті представлено огляд оцінки щільності натовпу, методи її підрахунку, а також методи відстежування натовпу і розуміння поведінки груп людей. Огляд охоплює два основні підходи, а саме: прямий і непрямий.Рассмотрены три важные проблемы анализа толпы: подсчет людей/оценка плотности, отслеживание в сценах с толпой людей и понимания поведения толпы. Автоматизированное оценивание и подсчет плотности толпы – это актуальная и важная тема в анализе толпы. Обзор охватывает два основных подхода оценки плотности толпы, а именно: прямой и косвенный.Three important problems in crowd analysis are considered in the paper: people counting/density estimation, tracking in crowd scenes, and understanding crowd behavior in higher-level analysis Automated crowd density estimation and crowd counting are actual and important topics in crowd analysis. The article provides an overview of crowd density estimation, methods for calculating crowd density, and methods for tracking crowds and understanding the behavior of groups of people. This review covers two main approaches, direct and indirect

People counting and human detection in a challenging situation

Reliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people and locate each individual in a low resolution image with complicated scenes. The contribution of this paper is threefold. First, postprocessing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. Second, an Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene. In this method, a new cluster model is used to represent each person in the scene. The method does not require a very accurate foreground contour. Third, the number of people is used as a priori for locating individuals based on feature points. Hence, the methods for estimating the number of people and for locating individuals are connected. The developed methods have been validated based on a 4-hour video, with the number of people in the scene ranging from 36 to 222. The best result for estimating the number of people has an average error of 10% over 51 test cases. Based on the estimated number of people, some results of the EM-based method have also been shown. © 2006 IEEE.published_or_final_versio