5 research outputs found
Markov random fields for abnormal behavior detection on highways
This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results show that abnormal behavior for a pedestrian walking, running and cycling on the highway, is detected with 82% accuracy at the 10% false alarm rate, and the system has an overall accuracy of 86% on the test data
DĂ©tection des chutes par calcul homographique
La vidĂ©osurveillance a pour objectif principal de protĂ©ger les personnes et les biens en dĂ©tectant tout comportement anormal. Ceci ne serait possible sans la dĂ©tection de mouvement dans lâimage. Ce processus complexe se base le plus souvent sur une opĂ©ration de soustraction de lâarriĂšre-plan statique dâune scĂšne sur lâimage. Mais il se trouve quâen vidĂ©osurveillance, des camĂ©ras sont souvent en mouvement, engendrant ainsi, un changement significatif de lâarriĂšre-plan; la soustraction de lâarriĂšre-plan devient alors problĂ©matique. Nous proposons dans ce travail, une mĂ©thode de dĂ©tection de mouvement et particuliĂšrement de chutes qui sâaffranchit de la soustraction de lâarriĂšre-plan et exploite la rotation de la camĂ©ra dans la dĂ©tection du mouvement en utilisant le calcul homographique. Nos rĂ©sultats sur des donnĂ©es synthĂ©tiques et rĂ©elles dĂ©montrent la faisabilitĂ© de cette approche.The main objective of video surveillance is to protect persons and property by detecting any abnormal behavior. This is not possible without detecting motion in the image. This process is often based on the concept of subtraction of the scene background. However in video tracking, the cameras are themselves often in motion, causing a significant change of the background. So, background subtraction techniques become problematic. We propose in this work a motion detection approach, with the example application of fall detection. This approach is free of background subtraction for a rotating surveillance camera. The method uses the camera rotation to detect motion by using homographic calculation. Our results on synthetic and real video sequences demonstrate the feasibility of this approach
Parametric tracking with spatial extraction across an array of cameras
Video surveillance is a rapidly growing area that has been fuelled by an increase in the concerns of security and safety in both public and private areas. With heighten security concerns, the utilization of video surveillance systems spread over a large area is becoming the norm. Surveillance of a large area requires a number of cameras to be deployed, which presents problems for human operators. In the surveillance of a large area, the need to monitor numerous screens makes an operator less effective in monitoring, observing or tracking groups or targets of interest. In such situations, the application of computer systems can prove highly effective in assisting human operators.
The overall aim of this thesis was to investigate different methods for tracking a target across an array of cameras. This required a set of parameters to be identified that could be passed between cameras as the target moved in and out of the fields of view. Initial investigations focussed on identifying the most effective colour space to use. A normalized cross correlation method was used initially with a reference image to track the target of interest. A second method investigated the use of histogram similarity in tracking targets. In this instance a reference targetâs histogram or pixel distribution was used as a means for tracking. Finally a method was investigated that used the relationship between colour regions that make up a whole target. An experimental method was developed that used the information between colour regions such as the vector and colour difference as a means for
tracking a target. This method was tested on a single camera configuration and multiple camera configuration and shown to be effective.
In addition to the experimental tracking method investigated, additional data can be extracted to estimate a spatial map of a target as the target of interest is tracked across an array of cameras.
For each method investigated the experimental results are presented in this thesis and it has been demonstrated that minimal data exchange can be used in order to track a target across an array of cameras. In addition to tracking a target, the spatial position of the target of interest could be estimated as it moves across the array
Vidéosurveillance intelligente pour la détection de chutes chez les personnes ùgées
Les pays industrialisés comme le Canada doivent faire face au vieillissement de leur population. En particulier, la majorité des personnes ùgées, vivant à domicile et souvent seules, font face à des situations à risques telles que des chutes. Dans ce contexte, la vidéosurveillance est une solution innovante qui peut leur permettre de vivre normalement dans un environnement sécurisé.
LâidĂ©e serait de placer un rĂ©seau de camĂ©ras dans lâappartement de la personne pour dĂ©tecter automatiquement une chute. En cas de problĂšme, un message pourrait ĂȘtre envoyĂ© suivant lâurgence aux secours ou Ă la famille via une connexion internet sĂ©curisĂ©e.
Pour un systÚme bas coût, nous avons limité le nombre de caméras à une seule par piÚce ce qui nous a poussé à explorer les méthodes monoculaires de détection de chutes.
Nous avons dâabord explorĂ© le problĂšme dâun point de vue 2D (image) en nous intĂ©ressant aux changements importants de la silhouette de la personne lors dâune chute.
Les donnĂ©es dâactivitĂ©s normales dâune personne ĂągĂ©e ont Ă©tĂ© modĂ©lisĂ©es par un mĂ©lange de gaussiennes nous permettant de dĂ©tecter tout Ă©vĂ©nement anormal. Notre mĂ©thode a Ă©tĂ© validĂ©e Ă lâaide dâune vidĂ©othĂšque de chutes simulĂ©es et dâactivitĂ©s normales rĂ©alistes.
Cependant, une information 3D telle que la localisation de la personne par rapport Ă son environnement peut ĂȘtre trĂšs intĂ©ressante pour un systĂšme dâanalyse de comportement. Bien quâil soit prĂ©fĂ©rable dâutiliser un systĂšme multi-camĂ©ras pour obtenir une information 3D, nous avons prouvĂ© quâavec une seule camĂ©ra calibrĂ©e, il Ă©tait possible de localiser une personne dans son environnement grĂące Ă sa tĂȘte. ConcrĂȘtement, la tĂȘte de la personne, modĂ©lisĂ©e par une ellipsoide, est suivie dans la sĂ©quence dâimages Ă lâaide dâun ïŹltre Ă particules. La prĂ©cision de la localisation 3D de la tĂȘte a Ă©tĂ© Ă©valuĂ©e avec une bibliothĂšque de sĂ©quence vidĂ©os contenant les vraies localisations 3D obtenues par un systĂšme de capture de mouvement (Motion Capture). Un exemple dâapplication utilisant la trajectoire 3D de la tĂȘte est proposĂ©e dans le cadre de la dĂ©tection de chutes.
En conclusion, un systĂšme de vidĂ©osurveillance pour la dĂ©tection de chutes avec une seule camĂ©ra par piĂšce est parfaitement envisageable. Pour rĂ©duire au maximum les risques de fausses alarmes, une mĂ©thode hybride combinant des informations 2D et 3D pourrait ĂȘtre envisagĂ©e.Developed countries like Canada have to adapt to a growing population of seniors.
A majority of seniors reside in private homes and most of them live alone, which can be dangerous in case of a fall, particularly if the person cannot call for help. Video surveillance is a new and promising solution for healthcare systems to ensure the safety of elderly people at home.
Concretely, a camera network would be placed in the apartment of the person in order to automatically detect a fall. When a fall is detected, a message would be sent to the emergency center or to the family through a secure Internet connection. For a low cost system, we must limit the number of cameras to only one per room, which leads us to explore monocular methods for fall detection.
We ïŹrst studied 2D information (images) by analyzing the shape deformation during a fall. Normal activities of an elderly person were used to train a Gaussian Mixture Model (GMM) to detect any abnormal event. Our method was tested with a realistic video data set of simulated falls and normal activities.
However, 3D information like the spatial localization of a person in a room can be very useful for action recognition. Although a multi-camera system is usually preferable to acquire 3D information, we have demonstrated that, with only one calibrated camera, it is possible to localize a person in his/her environment using the personâs head. Concretely, the head, modeled by a 3D ellipsoid, was tracked in the video sequence using particle ïŹlters. The precision of the 3D head localization was evaluated with a video data set containing the real 3D head localizations obtained with a Motion Capture system. An application example using the 3D head trajectory for fall detection is also proposed.
In conclusion, we have conïŹrmed that a video surveillance system for fall detection with only one camera per room is feasible. To reduce the risk of false alarms, a hybrid method combining 2D and 3D information could be considered