W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people

Haritaoglu, Ismail; Harwood, David; Davis, Larry S.

doi:10.1109/afgr.1998.670952

Cited by 406 publications

(258 citation statements)

References 5 publications

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“…The approximation from (31) to (32) is under the same reasoning as from (15) to (16). Under Gaussian assumption, the maximum likelihood parameter estimation of G j t can be obtained by taking derivatives of (32) with respect to mean and covariance matrix and equating to zero.…”

Section: Learning Rulesmentioning

confidence: 99%

“…To this end, a number of models of human motion have been proposed in the literature [14]. "Strong" models represent explicitly the kinematics and dynamics of the human body [25], [24], [4], [15], [16], [5], [31], while "weak" models represent its phenomenological spatio-temporal appearance [23], [35], [27], [26]. Strong models have the advantage of incorporating more information and, in principle, tolerate lower signalto-noise ratios and be allowed to reconstruct 3D body pose and motion from 2D images.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised learning of human motion

Yang

Gonçalves²,

Perona

2003

IEEE Trans. Pattern Anal. Machine Intell.

146

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Abstract-An unsupervised learning algorithm that can obtain a probabilistic model of an object composed of a collection of parts (a moving human body in our examples) automatically from unlabeled training data is presented. The training data include both useful "foreground" features as well as features that arise from irrelevant background clutter-the correspondence between parts and detected features is unknown. The joint probability density function of the parts is represented by a mixture of decomposable triangulated graphs which allow for fast detection. To learn the model structure as well as model parameters, an EM-like algorithm is developed where the labeling of the data (part assignments) is treated as hidden variables. The unsupervised learning technique is not limited to decomposable triangulated graphs. The efficiency and effectiveness of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled image sequences, and testing the learned models on a variety of sequences.

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Section: Learning Rulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised learning of human motion

Yang

Gonçalves²,

Perona

2003

IEEE Trans. Pattern Anal. Machine Intell.

146

View full text Add to dashboard Cite

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“…Among the change detection algorithms relying on the background subtraction principle, the statistical approach is the most widely adopted one [2,3,4,5,6]. With this approach some features are used to represent the background pixels (e.g.…”

Section: Previous Work and Proposed Approachmentioning

confidence: 99%

A Change-Detection Algorithm Enabling Intelligent Background Maintenance

Stefano

Mattoccia

Mola

2004

Lecture Notes in Computer Science

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Abstract.We have recently proposed a change-detection algorithm based on the idea of incorporating into the background model a set of simple low-level features capable of capturing effectively "structural" information. In this paper we show how this algorithm can naturally interact with the higher-level processing modules found in advanced videobased surveillance systems so as to allow for flexible and intelligent background maintenance.

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“…The manual intervention of the operator is reduced to the definition of the contour of the region to measure. This procedure will be soon automated, extracting from the images the silhouette of the person (Haritaoglu et al, 1998;Nef, 2001).…”

Section: Dynamic Surface Measurement/surface Tracking: Motion Capturementioning

confidence: 99%

Surface measurement and tracking of human body parts from multi-image video sequences

D'Apuzzo

2002

ISPRS Journal of Photogrammetry and Remote Sensing

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This paper describes a method to measure and track moving surfaces of human body parts from multi-image video sequences acquired simultaneously by several cameras. The gained 3-D data can be of two different types: surface measurement of the visible parts of the human body at each time step of the sequence and surface tracking in the form of a vector field of 3-D trajectories (position, velocity and acceleration). The surface measurement process, which is based on multi-image photogrammetry, consists of five steps: calibration of the camera system, simultaneous acquisition of images from different views, establishment of corresponding points in the images, computation of their 3-D coordinates and, eventually, generation of a surface model. The high level of automation achieved in all the steps makes the processing of long image sequences possible. The tracking process is based on least squares matching techniques. The main idea is to track corresponding points in the multi-images through the sequence and compute their 3-D trajectories. When applied to all the points matched on the body, it results in a vector field of trajectories. Some key-points can be defined and tracked in the vector field, producing general 3-D information about the recorded movement. The main advantages of the presented method are: the capability to dynamically measure surface parts with high accuracy and the possibility to extract motion information from the acquired data without using markers. Two applications are presented to demonstrate the functionality of the proposed method: human face modelling and full body motion capture. D

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W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people

Cited by 406 publications

References 5 publications

Unsupervised learning of human motion

Unsupervised learning of human motion

A Change-Detection Algorithm Enabling Intelligent Background Maintenance

Surface measurement and tracking of human body parts from multi-image video sequences

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