Multi camera image tracking

Black, J.; Ellis, Tim

doi:10.1016/j.imavis.2005.06.002

Cited by 126 publications

(63 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar multi-camera tracking frameworks have been presented, e.g. in the context of video surveillance [2] or soccer player tracking [3]. Targets are tracked by individual trackers at different views, and the results are fused by a fusion module.…”

Section: Introductionmentioning

confidence: 99%

Multi-view Object Tracking Using Sequential Belief Propagation

Piater

2006

Computer Vision – ACCV 2006

View full text Add to dashboard Cite

Abstract. Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-view Object Tracking Using Sequential Belief Propagation

Piater

2006

Computer Vision – ACCV 2006

View full text Add to dashboard Cite

show abstract

“…To this end, feature-level fusion approaches have been proposed to be more tolerant to faulty information in a single view. Approaches including (Black and Ellis 2006;Keck et al 2006;Khan and Shah 2006) make assumptions about the scene's planar structure in order to obtain object correspondence across multiple views and finally use Kalman filters or clustering to track objects. Very recently a multi-view kernel-based 3D tracker (Tyagi et al 2007) was proposed which extracted color features from all cameras and combined them into a single feature for mean-shift tracking directly in 3D.…”

Section: Occlusion Handlingmentioning

confidence: 99%

Recovery and Reasoning About Occlusions in 3D Using Few Cameras with Applications to 3D Tracking

Keck

Davis

2011

Int J Comput Vis

View full text Add to dashboard Cite

In this work we propose algorithms to learn the locations of static occlusions and reason about both static and dynamic occlusion scenarios in multi-camera scenes for 3D surveillance (e.g., reconstruction, tracking). We will show that this leads to a computer system which is able to more effectively track (follow) objects in video when they are obstructed from some of the views. Because of the nature of the application area, our algorithm will be under the constraints of using few cameras (no more than 3) that are configured wide-baseline. Our algorithm consists of a learning phase, where a 3D probabilistic model of occlusions is estimated per-voxel, per-view over time via an iterative framework. In this framework, at each frame the visual hull of each foreground object (person) is computed via a Markov Random Field that integrates the occlusion model. The model is then updated at each frame using this solution, providing an iterative process that can accurately estimate the occlusion model over time and overcome the few-camera constraint. We demonstrate the application of such a model to a number of areas, including visual hull reconstruction, the reconstruction of the occluding structures themselves, and 3D tracking.

show abstract

“…In fact, the fusion of the measurements gathered from distributed cameras can reduce the uncertainty of the perception, allowing reliable detection, localization and tracking systems. Many efforts have been devoted to the development of cooperative perception strategies exploiting the complementarities among distributed static cameras at ground locations (Black & Ellis, 2006), among cameras mounted on mobile robotic platforms (Shaferman & Shima, 2008) and among static cameras and cameras onboard mobile robots (Grocholski et al, 2006). In contrast to other techniques, camera-based Wireless Sensor Networks, comprised of distributed WSN nodes endowed with a camera as main sensor, require no collaboration from the object being tracked.…”

Section: Introductionmentioning

confidence: 99%