Kernel-based object tracking

Comanicìu, Dorin; Ramesh, Visvanathan; Meer, Peter

doi:10.1109/tpami.2003.1195991

Cited by 4,125 publications

(2,819 citation statements)

References 77 publications

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“…The ANN curve also is closest to the ideal step function, with 91% sensitivity and 90% specificity. We applied the Bhattacharyya distance metric (B) [12] to evaluate the discriminative power of the classifiers over normal and abnormal hearts.…”

Section: Methodsmentioning

confidence: 99%

A neural network learned information measures for heart motion abnormality detection

Nambakhsh

Punithakumar²,

Ayed

et al. 2011

SPIE Proceedings

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Abstract. In this study, we propose an information theoretic neural network for normal/abnormal left ventricular motion classification which outperforms significantly other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.

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Section: Methodsmentioning

confidence: 99%

A neural network learned information measures for heart motion abnormality detection

Nambakhsh

Punithakumar²,

Ayed

et al. 2011

SPIE Proceedings

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“…We have also compared our proposed AV-PF algorithm with one of the non-PF approaches, i.e. mean-shift tracking [62] which is a nonparametric statistical method. DOA is also fused in the same way as in our approach.…”

Section: B Visual Pf Vs Audio-visual Pfmentioning

confidence: 99%

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Kılıç

Barnard

Wang

et al. 2015

IEEE Trans. Multimedia

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Abstract-The problem of tracking multiple moving speakers in indoor environments has recently received much attention. Earlier techniques were based purely on vision, but the theoretical and algorithmic advances and a constant growth in speed of processing have led to the emergence of techniques which allow the fusion of audio and visual data. The fusion of multi-modal information has been shown to be instrumental in improving tracking performance, as well as robustness in the case of challenging situations like occlusions (by the limited field of view of cameras or by other speakers). However, data fusion algorithms often suffer from noise corrupting the sensor measurements which cause non-negligible detection errors. Here, a novel approach to combining audio and visual data is proposed. In our framework, we employ audio data as an aid to particle filter (PF) based visual tracking, by using the direction of arrival angles of the audio sources to reshape the typical Gaussian noise distribution of particles in the propagation step and to weight the observation model in the measurement step. This approach is further improved by solving a typical problem associated with the PF. It has been observed that the efficiency and accuracy of the PF usually depend on the number of particles and noise variance used in the estimation and propagation functions for re-allocating these particles at each iteration. Both of these parameters are specified beforehand and are kept fixed in the regular implementation of the PF which makes the tracker unstable in practice. To address these problems, we design an algorithm which adapts both the number of particles and noise variance based on tracking error and the area occupied by the particles in the image. Experiments on the AV 16.3 dataset show the advantage of our proposed methods over the baseline PF method and an existing adaptive PF algorithm for tracking occluded speakers with a significantly reduced number of particles.Index Terms-Audio-visual speaker tracking, particle filter, adaptive particle filter.

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“…Here we use Bhattacharyya coefficient [29,30,31,32] to measure the similarity between two regions say R and Q is:…”

Section: A Similarity Measurementioning

confidence: 99%

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

Saxena¹,

Jain²,

Singh³

2011

SpecialIssue

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Abstract-In this work; we address a novel interactive framework for object retrieval using unsupervised similar region merging and flood fill method which models the spatial and appearance relations among image pixels. Efficient and effective image segmentation is usually very hard for natural and complex images. This paper presents a new technique for similar region merging and objects retrieval. The users only need to roughly indicate the after which steps desired objects boundary is obtained during merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique. A region R is merged with its adjacent regions Q if Q has highest similarity with R among all Q's adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 22 object classes (524 images total) show promising results.

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Kernel-based object tracking

Cited by 4,125 publications

References 77 publications

A neural network learned information measures for heart motion abnormality detection

A neural network learned information measures for heart motion abnormality detection

Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

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