A system for tracking and recognizing pedestrian faces using a network of loosely coupled cameras

Héritier

et al. 2009

Univ Access Inf Soc

This paper presents the status of a R&D project targeting the development of computer-vision tools to assist humans in generating and rendering video description for people with vision loss. Three principal issues are discussed: (1) production practices, (2) needs of people with vision loss, and (3) current system design, core technologies and implementation. The paper provides the main conclusions of consultations with producers of video description regarding their practices and with end-users regarding their needs, as well as an analysis of described productions that lead to propose a video description typology. The current status of a prototype software is also presented (audio-vision manager) that uses many computer-vision technologies (shot transition detection, key-frame identification, key-face recognition, key-text spotting, visual motion, gait/gesture characterization, keyplace identification, key-object spotting and image categorization) to automatically extract visual content, associate textual descriptions and add them to the audio track with a synthetic voice. A proof of concept is also briefly described for a first adaptive video description player which allows end users to select various levels of video description.

Section: Key-facesmentioning

confidence: 99%

Towards computer-vision software tools to increase production and accessibility of video description for people with vision loss

Héritier

et al. 2009

Univ Access Inf Soc

“…It can determine whether a foreground region contains multiple people and can segment the region into its constituents. More recently, a system with a decentralized architecture has been developed [2,7] with no dependence on a central server that could fail during an operational mode. The intelligent nodes send and receive information between them and a pair of cameras are attached to each node (one of them is an infrared camera) to improve performance in low-light conditions).…”

Section: Introductionmentioning

confidence: 99%

“…The Defense Advanced Research Projects Agency (DARPA) Information Systems Office launched the three-year VSAM program in 1997 to develop automated video understanding technology for use in future urban and battlefield surveillance applications. The VSAM program looked at several fundamental issues in detection, tracking, auto-calibration, and multi-camera systems and motivated many other academic researches (for instance, [5][6][7]). Collins et al [5] have developed a system that allows a human operator to monitor activities over a large area using multiple calibrated cameras with a geospatial site model.…”

Section: Introductionmentioning

confidence: 99%

A system to automatically track humans and vehicles with a PTZ camera

Lalonde

Visual Information Processing XVI

et al. 2007

Self Cite

The paper reports about the development of a software module that allows autonomous object detection, recognition and tracking in outdoor urban environment. The purpose of the project was to endow a commercial PTZ camera with object tracking and recognition capability to automate some surveillance tasks. The module can discriminate between various moving objects and identify the presence of pedestrians or vehicles, track them, and zoom on them, in near real-time. The paper gives an overview of the module characteristics and its operational uses within the commercial system.

“…The best facial expression interpretation rate obtained was 74.19% using a nearest neighbor classifier with a Euclidean distance similarity measure. The plug-in is a version of a face characterization module recently developed for a video monitoring system based on a set of loosely coupled cameras that build models and exchange visual information to track and recognize pedestrians [46].…”

Section: Facial Characterizationmentioning

confidence: 99%

Toward an Application of Content-Based Video Indexing to Computer- Assisted Descriptive Video

The 3rd Canadian Conference on Computer and Robot Vision (CRV'06)

Laliberte

et al.

Self Cite