Co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring system

The increasing number of CCTV cameras in use poses a problem of information overloading for end users. Smart technologies are used in video surveillance to automatically analyze and detect events of interest in real-time, through 2D and 3D video processing techniques called video analytics. This paper presents a smart surveillance stereo vision system for real-time intelligent door access monitoring. The system uses two IP cameras in a stereo configuration and a pan-tilt-zoom (PTZ) camera, to obtain real-time localised, high quality images of any triggering events

Co-operative surveillance cameras for high quality face acquisition in a real-time door monitoring system

The increasing number of CCTV cameras in use poses a problem of information overloading for end users. Smart technologies are used in video surveillance to automatically analyze and detect events of interest in real-time, through 2D and 3D video processing techniques called video analytics. This paper presents a smart surveillance stereo vision system for real-time intelligent door access monitoring. The system uses two IP cameras in a stereo configuration and a pan-tilt-zoom (PTZ) camera, to obtain real-time localised, high quality images of any triggering events

Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Object Tracking with a pan-tilt-zoom camera : application to car driving assistance

International audienceIn this paper, visual perception in car driving assistance is considered. The work deals with the development of a system combining a pan-tilt-zoom (PTZ) camera and a standard camera, in order to track the front vehicles. The standard camera has a small focal length, and is devoted to the analyse of the whole frontal scene. Here, the PTZ camera is used to track the closest vehicle. Camera rotations and zoom are controlled by visual servoing and by an efficient real time target tracking algorithm. The aim of this work is to keep the rear view image of target vehicle stable in scale and position. The methods presented were tested on real road sequences within the VELAC demonstration vehicle. Experimental results show the effectiveness of such an approach

Reproducible Evaluation of Pan-Tilt-Zoom Tracking

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is very difficult to assess the progress that has been made on this topic because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.Comment: This is an extended version of the 2015 ICIP paper "Reproducible Evaluation of Pan-Tilt-Zoom Tracking

Evaluation of trackers for Pan-Tilt-Zoom Scenarios

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario.Comment: 6 pages, 2 figures, International Conference on Pattern Recognition and Artificial Intelligence 201

Face detection and stereo matching algorithms for smart surveillance system with IP cameras

In this paper, we describe a smart surveillance system to detect human faces in stereo images with applications to advanced video surveillance systems. The system utilizes two smart IP cameras to obtain the position and location of the object that is a human face. The position and location of the object are extracted from two IP cameras and subsequently transmitted to a Pan-Tilt-Zoom (PTZ) camera, which can point to the exact position in space. This work involves video analytics for estimating the location of the object in a 3D environment and transmitting its positional coordinates to the PTZ camera. The research consists of algorithm development in surveillance system including face detection, stereo matching, location estimation and implementation with ACTi PTZ camera. The final system allows the PTZ camera to track the objects and acquires images in high-resolution

Voronoi-Based Coverage Control of Pan/Tilt/Zoom Camera Networks

A challenge of pan/tilt/zoom (PTZ) camera networks for efficient and flexible visual monitoring is automated active network reconfiguration in response to environmental stimuli. In this paper, given an event/activity distribution over a convex environment, we propose a new provably correct reactive coverage control algorithm for PTZ camera networks that continuously (re)configures camera orientations and zoom levels (i.e., angles of view) in order to locally maximize their total coverage quality. Our construction is based on careful modeling of visual sensing quality that is consistent with the physical nature of cameras, and we introduce a new notion of conic Voronoi diagrams, based on our sensing quality measures, to solve the camera network allocation problem: that is, to determine where each camera should focus in its field of view given all the other cameras\u27 configurations. Accordingly, we design simple greedy gradient algorithms for both continuous- and discrete-time first-order PTZ camera dynamics that asymptotically converge a locally optimal coverage configuration. Finally, we provide numerical and experimental evidence demonstrating the effectiveness of the proposed coverage algorithms

Smart surveillance system based on stereo matching algorithms with IP and PTZ cameras

In this paper, we describe a system for smart surveillance using stereo images with applications to advanced video surveillance systems. The system utilizes two smart IP cameras to obtain the position and location of objects. In this case, the object target is human face. The position and location of the object are automatically extracted from two IP cameras and subsequently transmitted to an ACTi Pan-Tilt-Zoom (PTZ) camera, which then points and zooms to the exact position in space. This work involves video analytics for estimating the location of the object in a 3D environment and transmitting its positional coordinates to the PTZ camera. The research consists of algorithms development in surveillance system including face detection, block matching, location estimation and implementation with ACTi SDK tool. The final system allows the PTZ camera to track the objects and acquires images in high-resolution quality