5 research outputs found
A distributed camera system for multi-resolution surveillance
We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor.
Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database.
Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table.
We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating
under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance
Object Detection Through Exploration With A Foveated Visual Field
We present a foveated object detector (FOD) as a biologically-inspired
alternative to the sliding window (SW) approach which is the dominant method of
search in computer vision object detection. Similar to the human visual system,
the FOD has higher resolution at the fovea and lower resolution at the visual
periphery. Consequently, more computational resources are allocated at the
fovea and relatively fewer at the periphery. The FOD processes the entire
scene, uses retino-specific object detection classifiers to guide eye
movements, aligns its fovea with regions of interest in the input image and
integrates observations across multiple fixations. Our approach combines modern
object detectors from computer vision with a recent model of peripheral pooling
regions found at the V1 layer of the human visual system. We assessed various
eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD
performs on par with the SW detector while bringing significant computational
cost savings.Comment: An extended version of this manuscript was published in PLOS
Computational Biology (October 2017) at
https://doi.org/10.1371/journal.pcbi.100574
Unsupervised Methods for Camera Pose Estimation and People Counting in Crowded Scenes
Most visual crowd counting methods rely on training with labeled data to learn a mapping between features in the image and the number of people in the scene. However, the exact nature of this mapping may change as a function of different scene and viewing conditions, limiting the ability of such supervised systems to generalize to novel conditions, and thus preventing broad deployment. Here I propose an alternative, unsupervised strategy anchored on a 3D simulation that automatically learns how groups of people appear in the image and adapts to the signal processing parameters of the current viewing scenario. To implement this 3D strategy, knowledge of the camera parameters is required. Most methods for automatic camera calibration make assumptions about regularities in scene structure or motion patterns, which do not always apply. I propose a novel motion based approach for recovering camera tilt that does not require tracking. Having an automatic camera calibration method allows for the implementation of an accurate crowd counting algorithm that reasons in 3D. The system is evaluated on various datasets and compared against state-of-art methods
Low and Variable Frame Rate Face Tracking Using an IP PTZ Camera
RĂSUMĂ
En vision par ordinateur, le suivi d'objets avec des camĂ©ras PTZ a des applications dans divers domaines, tels que la surveillance vidĂ©o, la surveillance du trafic, la surveillance de personnes et la reconnaissance de visage. Toutefois, un suivi plus prĂ©cis, efficace, et fiable est requis pour une utilisation courante dans ces domaines. Dans cette thĂšse, le suivi est appliquĂ© au haut du corps d'un humain, en incluant son visage. Le suivi du visage permet de dĂ©terminer son emplacement pour chaque trame d'une vidĂ©o. Il peut ĂȘtre utilisĂ© pour obtenir des images du visage d'un humain dans des poses diffĂ©rentes. Dans ce travail, nous proposons de suivre le visage d'un humain Ă lâaide d'une camĂ©ra IP PTZ (camĂ©ra rĂ©seau orientable). Une camĂ©ra IP PTZ rĂ©pond Ă une commande via son serveur Web intĂ©grĂ© et permet un accĂšs distribuĂ© Ă partir d'Internet. Le suivi avec ce type de camĂ©ra inclut un bon nombre de dĂ©fis, tels que des temps de rĂ©ponse irrĂ©gulier aux commandes de contrĂŽle, des taux de trame faibles et irrĂ©guliers, de grand mouvements de la cible entre deux trames, des occlusions, des modifications au champ de vue, des changements d'Ă©chelle, etc.
Dans notre travail, nous souhaitons solutionner les problÚmes des grands mouvements de la cible entre deux trames consécutives, du faible taux de trame, des modifications de l'arriÚre-plan, et du suivi avec divers changements d'échelle. En outre, l'algorithme de suivi doit prévoir les temps de réponse irréguliers de la caméra.
Notre solution se compose dâune phase dâinitialisation pour modĂ©liser la cible (haut du corps), dâune adaptation du filtre de particules qui utilise le flux optique pour gĂ©nĂ©rer des Ă©chantillons Ă chaque trame (APF-OFS), et du contrĂŽle de la camĂ©ra. Chaque composante exige des stratĂ©gies diffĂ©rentes.
Lors de l'initialisation, on suppose que la camĂ©ra est statique. Ainsi, la dĂ©tection du mouvement par soustraction dâarriĂšre-plan est utilisĂ©e pour dĂ©tecter l'emplacement initial de la personne. Ensuite, pour supprimer les faux positifs, un classificateur Bayesien est appliquĂ© sur la rĂ©gion dĂ©tectĂ©e afin de localiser les rĂ©gions avec de la peau. Ensuite, une dĂ©tection du visage basĂ©e sur la mĂ©thode de Viola et Jones est effectuĂ©e sur les rĂ©gions de la peau. Si un visage est dĂ©tectĂ©, le suivi est lancĂ© sur le haut du corps de la personne.----------ABSTRACT
Object tracking with PTZ cameras has various applications in different computer vision topics such as video surveillance, traffic monitoring, people monitoring and face recognition. Accurate, efficient, and reliable tracking is required for this task. Here, object tracking is applied to human upper body tracking and face tracking. Face tracking determines the location of the human face for each input image of a video. It can be used to get images of the face of a human target under different poses. We propose to track the human face by means of an Internet Protocol (IP) Pan-Tilt-Zoom (PTZ) camera (i.e. a network-based camera that pans, tilts and zooms). An IP PTZ camera responds to command via its integrated web server. It allows a distributed access from Internet (access from everywhere, but with non-defined delay). Tracking with such camera includes many challenges such as irregular response times to camera control commands, low and irregular frame rate, large motions of the target between two frames, target occlusion, changing field of view (FOV), various scale changes, etc.
In our work, we want to cope with the problem of large inter-frame motion of targets, low usable frame rate, background changes, and tracking with various scale changes. In addition, the tracking algorithm should handle the camera response time and zooming.
Our solution consists of a system initialization phase which is the processing before camera motion and a tracker based on an Adaptive Particle Filter using Optical Flow based Sampling (APF-OFS) tracker, and camera control that are the processing after the motion of the camera. Each part requires different strategies.
For initialization, when the camera is stationary, motion detection for a static camera is used to detect the initial location of the person face entering an area. For motion detection in the FOV of the camera, a background subtraction method is applied. Then to remove false positives, Bayesian skin classifier is applied on the detected motion region to discriminate skin regions from non skin regions. Face detection based on Viola and Jones face detector can be performed on the detected skin regions independently of their face size and position within the image