Pedestrian detection in uncontrolled environments using stereo and biometric information

A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios

People counting system using existing surveillance video camera

The Casa da Música Foundation, responsible for the management of Casa da Música do Porto building, has the need to obtain statistical data related to the number of building’s visitors. This information is a valuable tool for the elaboration of periodical reports concerning the success of this cultural institution. For this reason it was necessary to develop a system capable of returning the number of visitors for a requested period of time. This represents a complex task due to the building’s unique architectural design, characterized by very large doors and halls, and the sudden large number of people that pass through them in moments preceding and proceeding the different activities occurring in the building. To achieve the technical solution for this challenge, several image processing methods, for people detection with still cameras, were first studied. The next step was the development of a real time algorithm, using OpenCV libraries and computer vision concepts,to count individuals with the desired accuracy. This algorithm includes the scientific and technical knowledge acquired in the study of the previous methods. The themes developed in this thesis comprise the fields of background maintenance, shadow and highlight detection, and blob detection and tracking. A graphical interface was also built, to help on the development, test and tunning of the proposed system, as a complement to the work. Furthermore, tests to the system were also performed, to certify the proposed techniques against a set of limited circumstances. The results obtained revealed that the algorithm was successfully applied to count the number of people in complex environments with reliable accuracy.A Fundação Casa da Música, responsável pela gestão do edifício da Casa da Música, tem a necessidade de obter dados estatísticos relativos ao número de visitantes. Esta informação é uma ferramenta valiosa para a elaboração periódica de relatórios de afluência para a avaliação do sucesso desta instituição cultural. Por este motivo existe a necessidade da elaboração de um sistema capaz de fornecer o número de visitantes para um determinado período de tempo. Esta tarefa é dificultada pelas características arquitetônicas, únicas do edifício, com portas largas e amplos halls, e devido ao súbito número de pessoas que passam por estas áreas em momentos que antecedem e procedem concertos, ou qualquer outras actividades. Para alcançar uma solução técnica para este desafio foi inicialmente elaborado um estado da arte relativo a métodos de processamento de imagem para deteção de pessoas com câmeras de vídeo. O passo seguinte foi, utilizando bibliotecas de OpenCV e conceitos de visão computacional, o desenvolvimento de um algoritmo em tempo real para contar pessoas com a precisão desejada. Este algoritmo inclui o conhecimento científico e técnico adquirido em métodos previamente estudados. Os temas desenvolvidos nesta tese compreendem os campos de manutenção do fundo, deteção de zonas sub e sobre iluminadas e deteção e seguimento de blobs. Foi também construida uma interface gráfica para ajudar o desenvolvimento, teste e afinação do sistema proposto como complemento ao trabalho desenvolvido. Além disso, perante um conjunto limitado de circunstâncias, foram efectuados testes ao sistema em ordem a certificar as técnicas propostas. Os resultados obtidos revelaram que o algoritmo foi aplicado com sucesso para contar pessoas em ambientes complexos com precisão

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets

Automated Detection and Counting of Pedestrians on an Urban Roadside

This thesis implements an automated system that counts pedestrians with 85% accuracy. Two approaches have been considered and evaluated in terms of count accuracy, cost and ease of deployment. The first approach employs the Autoscope Solo Terra, a traffic camera which is widely used to monitor vehicular traffic. The Solo Terra supports an image processing-based detector that counts the number of objects crossing user-defined areas in the captured image. The count is updated based on the amount of movement across the selected regions. Therefore, a second approach has been considered that uses a histogram of oriented gradients (HoG), an advanced vision based algorithm proposed by Dalal et al. which distinguishes a pedestrian from a non-pedestrian based on an omega shape formed by the head and shoulders of a human being. The implemented detection software processes video frames that are streamed from a low-cost digital camera. The frames are divided into sub-regions which are scanned for an omega shape whenever movement is detected in those regions. It has been found that the HoG-based approach degrades in performance due to occlusion under dense pedestrian traffic conditions whereas the Solo Terra approach appears to be more robust. Undercounts and overcounts were encountered using the Solo Terra approach. To combat the disadvantages of both the approaches, they were integrated to form a single system where count is incremented predominantly using the Solo Terra. The HoG-based approach corrects the obtained count under certain conditions. A preliminary prototype of the integrated system has been verified

A Systematic Human Counting at Guest House using Sensing Device Technique

The application of vision detector using sensing device techniques is important in systematic counting of people both indoors and outdoors. This technique is broadly used in auditorium, lecture theatre and public market. In this paper, the technique uses a camera attached to an Android-based mobile phone which is then applied to capture images that are then transferred to a storage system via USB for image processing and counting. Also, a model for counting people indoors and outdoors is developed. Also, accurate human counting is observe

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants