Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts

Kohli, Pushmeet; Rihan, Jonathan; Bray, Matthieu; Torr, Philip H. S.

doi:10.1007/s11263-007-0120-6

Cited by 115 publications

(71 citation statements)

References 31 publications

(50 reference statements)

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“…There are mainly two types of approaches reported in the literature for crowd size estimation [35], [37]: direct detection or object detection based approach, and indirect or feature based approach (Figure 9). In the direct method (see Figure 10 (a)), the main steps for the detection are: segmentation, detect the individuals present in the scenes, and count the people using classifiers (see Figure 9 (a)) [105], [106], [107]. The model-based and trajectory clustering based approaches belong to the direct method.…”

Section: E Video Datamentioning

confidence: 99%

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

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Section: E Video Datamentioning

confidence: 99%

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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“…Deformable models in cardiac imaging, yet, are more often proposed for tracking motion in the heart [12]. Markov random field models have been used for segmenting knee images [13] as well as magnetic resonance angiograms (MRAs) [14]. Other methods for segmenting MRAs include deformable models and thresholding.…”

Section: Literature Reviewmentioning

confidence: 99%

Image segmentation using nearest neighbor classifiers based on kernel formation for medical images

Harini

Chandrasekar

2012

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)

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Image Segmentation is one of the significant elements in the part of image processing. It becomes most essential demanding factor while typically dealing with medical image segmentation. In this paper, proposal of our work comprises of formation of kernel for the medical images by performing the deviation of mapped image data within the scope of each region from the piecewise constant model and based on the regularization term based on the function of indices value of the region. The functional objective minimization is carried out by two steps minimization in image segmentation using graph cut methods, and minimization with respect to region parameters using constant point computation. Nearest neighbor classifiers are introduced to the benchmarked image data segmented portions. Among the different methods in supervised statistical pattern recognition, the nearest neighbor rule results in achieving high performance without requirement of the prior assumptions about the distributions from which the training sets are taken.Keywords: Nearest Neighbor Classifiers, Image Segmentation, Kernel-Formation IntroductionThe fundamental concept relying in image processing is image segmentation and classifying. The purpose behind the processing is to formulate the image into regions.Variational formulations that result in effective algorithms include the required attributes of its region and boundaries. Works have been carried out both in continuous and discrete formulations, though discrete version of image segmentation does not approximate continuous formulation. Analysis is carried out on digitized variations of the variational formulation of the segmentation problem. Uses of graph cut algorithms have proven to be very efficient. Several works carried out shows that graph cut algorithms are of great interest in image analysis.Unsupervised graph cut methods proceeds without the intervention of the humans which uses the Gaussian generalization model as the data can be described in the way as is required by the graph cut algorithm model. Though useful these models are not of parameters of all the models described above do not depend upon the data, as a result of it the models cannot be used.The objective function may be to find out the sum of all the pixels or select the pixel neighborhood data and variables. The parameters considered in Gaussian method are discrete approximation, linear combinations of the image provided. One way to modify the model is to introduce user interaction. Various interactive graph cut models have been used in contrary to the Gaussian model at any step of the segmentation process. Based on the segmented portions of the image we can diagnose the various causes based on the medical images. Literature reviewThe majority of research in medical image segmentation pertains to its use for MR images, particularly in brain imaging. This is due to MR's ability to give a mixture of high resolution (on the order of 1mm cubic voxels), outstanding soft tissue contrast, and a high signal-to-noise ratio [1...

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“…On the other hand, pose initialization using 3D generative models (Kohli et al 2008; Lee and Cohen 2006) involves finding the best match between model projections and image, and retrieving the associated 3D pose. 3D generative models typically involve compositions of volumetric primitives like ellipsoids or cones (Forsyth et al 2005;Gavrila 1999; Moeslund et al 2006).…”

Section: Previous Workmentioning

confidence: 99%

Multi-view 3D Human Pose Estimation in Complex Environment

Hofmann

Gavrila

2011

Int J Comput Vis

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We introduce a framework for unconstrained 3D human upper body pose estimation from multiple camera views in complex environment. Its main novelty lies in the integration of three components: single-frame pose recovery, temporal integration and model texture adaptation. Single-frame pose recovery consists of a hypothesis generation stage, in which candidate 3D poses are generated, based on probabilistic hierarchical shape matching in each camera view. In the subsequent hypothesis verification stage, the candidate 3D poses are re-projected into the other camera views and ranked according to a multi-view likelihood measure. Temporal integration consists of computing K-best trajectories combining a motion model and observations in a Viterbi-style maximum-likelihood approach. Poses that lie on the best trajectories are used to generate and adapt a texture model, which in turn enriches the shape likelihood measure used for pose recovery. The multiple trajectory hypotheses are used to generate pose predictions, augmenting the 3D pose candidates generated at the next time step.We demonstrate that our approach outperforms the stateof-the-art in experiments with large and challenging realworld data from an outdoor setting.

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Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts

Cited by 115 publications

References 31 publications

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Image segmentation using nearest neighbor classifiers based on kernel formation for medical images

Multi-view 3D Human Pose Estimation in Complex Environment

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