Pfinder: real-time tracking of the human body

Wren, Christopher R.; Azarbayejani, A.; Darrell, Trevor; Pentland, Alex

doi:10.1109/afgr.1996.557243

Cited by 875 publications

(1,171 citation statements)

References 10 publications

Supporting

Mentioning

1,160

Contrasting

Unclassified

Order By: Relevance

“…During the past decade extensive research has been carried out in this area, leading to several techniques (see for instance [2], [10], [11], [21], [24], [13] and references therein). In particular, a class of dynamics based trackers has been developed that combine a-priori assumed dynamic models of the target motion with optimal filtering -(unscented) Kalman, particle- [19], [14], [15] to track in the presence of occlusion.…”

Section: Introductionmentioning

confidence: 99%

Receding horizon rank minimization based estimation with applications to visual tracking

Ding

Sznaier

Camps

2008

2008 47th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Abstract-This paper addresses the problem of predicting future outputs of an unknown Linear Time Invariant System based solely on past input/output data corrupted by noise, and an a-priori bound on the system order. This situation arises in many scenarios of practical interest where an explicit a-priori model of the system is not available. The main result of the paper is a simple, computationally efficient tracking algorithm that does not entail identifying first the unknown dynamics. Rather, the problem of estimating the next value of the output is recast into a rank minimization problem and solved using some recently introduced convex relaxations. The potential of the proposed approach is illustrated using as an example the problem of tracking multiple targets in video sequences in the presence of occlusion.

show abstract

Section: Introductionmentioning

confidence: 99%

Receding horizon rank minimization based estimation with applications to visual tracking

Ding

Sznaier

Camps

2008

2008 47th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

show abstract

“…The next step of the evolution embedded the detection and the tracking of "objects" of interest in a serial multistage framework [34,70]. The lower levels are those closer to the raw data, performing operations such as noise removal, and motion detection.…”

Section: Surveillance and Monitoringmentioning

confidence: 99%

Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space

Cristani

Murino

Vinciarelli

2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

View full text Add to dashboard Cite

In general terms, surveillance and monitoring technologies aim at understanding what people do in a given environment, whether this means to ensure the safety of workers on the factory floor, to detect crimes occurring in indoor or outdoor settings, or to monitor the flow of large crowds through public spaces. However, surveillance and monitoring technologies rarely consider that they analyze human behavior, a phenomenon subject to principles and laws rigorous enough to produce stable and predictable patterns corresponding to social, affective, and psychological phenomena. On the other hand, these phenomena are the subject of other computing domains, in particular Social Signal Processing and Affective Computing, that typically neglect scenarios relevant to surveillance and monitoring technologies, especially when it comes to social and affective dimensions of space in human activities. The goal of this paper is to show that the investigation of the overlapping area between surveillance and monitoring on one side, and Social Signal Processing and Affective Computing on the other side can bring significant progress in both domains and open a number of interesting research perspectives.

show abstract

“…Among them are running Gaussian average [1], Gaussian mixture model (GMM) [2], kernel density estimation [3], and eigenbackground [4]. An admirable review of these techniques is presented in [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking Using Color Information

Kim¹,

Khan²,

Awan³

et al. 2014

IJCCE

View full text Add to dashboard Cite

Abstract-For security purposes, it is prerequisite to track multiple targets efficiently. Most of the current implementation uses Kalman filter and color information independently. The proposed method combines extended Kalman filter and color information for tracking multiple objects under high occlusion. For tracking, the first thing done is the object detection. The background model used to segment foreground from background is spatio-temporal Gaussian mixture model (STGMM). Tracking consists of two steps: independent object tracking and occluded object tracking. For independent object tracking we exploit extended Kalman filter, whereas for occluded object tracking, color information attribute is used. The system was tested in real world application and successful results were obtained.

show abstract

Pfinder: real-time tracking of the human body

Cited by 875 publications

References 10 publications

Receding horizon rank minimization based estimation with applications to visual tracking

Receding horizon rank minimization based estimation with applications to visual tracking

Socially intelligent surveillance and monitoring: Analysing social dimensions of physical space

Multi-Target Tracking Using Color Information

Contact Info

Product

Resources

About