Top PDF A Probabilistic Approach to Human Motion Detection and Labeling

A Probabilistic Approach to Human Motion Detection and Labeling

A Probabilistic Approach to Human Motion Detection and Labeling

In this chapter, we show how a decomposable triangulated graph can be transformed into a junction tree such that max-propagation developed for graphical models can be used to the labelin[r]

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A Probabilistic Approach to Persian Ezafe Recognition

A Probabilistic Approach to Persian Ezafe Recognition

In a research accomplished by (Isapour, et al., 2007), the researchers rely on the fact that Ezafe can relate between head and its modifiers so as to help to build NPs. So by parsing sentences and finding Phrase borders, the location of Ezafe in the sentence can be found. In this work, the sentences were analyzed using a Probabilistic Context Free Grammar (PCFG) to derive phrase borders. Then based on the extracted parse tree, the head and modifiers in each phrase can be determined. In the last phase, a rule based approach was also applied to increase the accuracy in Ezafe marker labeling. For training the algorithm, 1000 sentences were selected and a parse tree was built for each of them. Because of the limited number of parsed sentences for training, the results cannot be extended for general applications.
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An Unsupervised Probabilistic Approach for the Detection of Outliers in Corpora

An Unsupervised Probabilistic Approach for the Detection of Outliers in Corpora

some initial results of its use on artificially created collec- tions. The results illustrate that for large segments of text it is possible to achieve fairly good results detecting out- liers in some types of corpora. For instance, an average of 92% of translations of chinese news stories can be identi- fied in corpora composed of newswire with a precision of 71.9%. This is a very good result given that this procedure is completely unsupervised and makes use of no training data. The fact versus opinion experiments proved to be a much more difficult task and on average achieve only a 61% F -measure for large pieces of text. These results are somewhat disappointing, but this is a difficult task as we are attempting to label every piece of text as either an outlier or non-outlier. The results of this labeling are closely tied to the cutoff used for determining which observations are farthest away from the rest of the data. While choosing a cutoff to automatically separate outliers from non-outliers is difficult, other experimental results (Guthrie, 2008) per- formed on these corpora indicate that using this detection method often results in the outlying piece of text having the greatest distance from the rest of the corpus. Further research is ongoing to see if this cutoff can be more intel- ligently chosen to improve the accuracy of results on this task.
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Automatic detection of motion sequences for motion analysis

Automatic detection of motion sequences for motion analysis

analysis of the different types of data are required. Existing annotation software, such as ELAN [2] or Anvil [3], has recently started to integrate facilities for displaying time series data. ELAN and Anvil allow for linking text annotations with segments of digital media files. ELAN is specialized on Audio and Video media data and provides automatical annotation especially for audio signals. Anvil is additionally able to display the motion of a single person specialized on the plot from the axes of the position, velocity, acceleration, and a color highlighting trajectory visualization equals to the annotation color. However, in its current version the ability to handle data from multiple participants is missing and it offers only limited support for motion analysis. In this paper, we present our pre-annotation tool PAMOCAT that addresses these gaps: It is able to deal with data from multiple participants, to show their skeletons and corresponding motion, and to highlight motion activity for each Degree of Freedom (DOF) separately so that quick access to specific motion activities of a particular joint is possible. In particular, it allows to both visualize and analyze three- dimensional motion capture data and to export automatically generated annotations to existing annotation software such as ELAN. To motivate our approach and to demonstrate how our tool could be integrated into a research cycle linking qualitative and quantitative methods, we will begin with a short example from the analysis of human-human interaction and the analytical issues that arise from it (section 2). Based on this, we will present our approach of robustly tracking multiple participants with motion capture technology (section 3), the basic ideas and user interface of our tool PAMOCAT (section 4) and explain some of its current analytical facilities (section 5). Specifically, we will introduce the notion of “key-intervals” as the basic concept of the tool. Finally, we will give some examples of how PAMOCAT could support data analysis (section 6) and will conclude with a short outlook regarding future work (section 7).
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Silhouette Based Human Motion Detection and Recognising their Actions from the Captured Video Streams

Silhouette Based Human Motion Detection and Recognising their Actions from the Captured Video Streams

Much of the work has been done in the areas of human detection and human action recognition. Murat Ekinci and Eyiip Gedikip [1] use spatio-temporal jets and silhouette based action recognition techniques, in their approach the gray scale images are used for recognition. The background scene model is statically learned and the pixel having higher redundancy is chosen to have initial background model. The outlines of the foreground object is detected and tracked over successive frames to identify the actions. Nazh Ikizlerand, Pinar Duygulu [2, 3] uses bag of visual rectangles to recognize human actions, in their approach the captured video streams are converted into gray scale images, and its background motion is subtracted using adaptive background subtraction techniques. Histograms of oriented gradients (bag-of-visual-words) is used to represent the selected object as a distribution of oriented rectangular patches and by knowing the orientations of these patches the human actions are recognized. Chunfeng Yuan, Weiming Hu, XiLi, Stephen Maybankand, Guan Luo [4] discuss about human action recognition using log-Euclidean Riemannian metric and histograms of oriented gradients, in their approach, Dollár et al.’s detector is used to detect cuboids from each frame. Then the descriptors are used to extract the features (bag-of-visual-words) by using the k- mean clustering method. Histograms of bag-of-visual- words are then classified according to histograms matching between the test video and training video sequence. EMD matching techniques are then used for matching each video pair obtained from training and testing samples to identify the human actions.
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Probabilistic motion pixel detection for the reduction of ghost artifacts in high dynamic range images from multiple exposures

Probabilistic motion pixel detection for the reduction of ghost artifacts in high dynamic range images from multiple exposures

output as the HDRI approach by Oh et al. [20], which also yields almost no noticeable artifacts for the given image sequence. Likewise, Figure 13 shows the comparison of [16,20,21,23,30] and [17] with the proposed method. The photos of Figure 13a are the input images, and the sec- ond rows of Figure 13b,c,d,e show the results by [16,23,30] and [20], respectively, where the images in the third row are the results of the proposed method at the same area. It can be observed that the method of Gallo et al. [16] removes ghost successfully; however, when the difference of brightness among the neighboring patch is large, this causes some visible seam as shown in Figure 13b. In Figure 13c,d,e, we can see ghost artifacts in the existing methods, whereas the artifact is not noticeable in the case
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Monocular 3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

Monocular 3D Human Motion Tracking Using Dynamic Probabilistic Latent Semantic Analysis

In addition to articulated human body deformations, perception of pose from monocular images is complicated by often abrupt changes in body orientation and camera viewpoint. However, these situations are common-place in a wide range of applications such as surveillance. Recent work has resulted in several tracking or recognition algorithms that integrate view variant observations. Fablet and Black [4] proposed a Bayesian framework for the tracking of human motion which combines 2D optical flow information from arbitrary viewpoints using low-dimensional spatio-temporal models. Another spatio-temporal modeling approach combines pictorial view-dependent shape models and Hidden Markov Models (HMM) by using a Bayesian framework and infers the model by dynamic programming and sampling methods [9]. In [3], separate view-based manifolds are utilized to model changes of shape in different views and the view point is determined by selecting the manifold that minimizes the inverse-mapping error. Torus manifold embedding is used to jointly represent the view and body configuration on a conceptual embedding space for pose estimation [11]. While our model represents the view manifold by a finite set of points, the model learning is performed jointly with the pose-feature mapping. As a result, the learned models take into account the nonlinear dependencies approximated by tori in [11] in a computationally efficient manner.
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A Probabilistic Approach to Text Generation of Human Motions extracted from Kinect Videos

A Probabilistic Approach to Text Generation of Human Motions extracted from Kinect Videos

Once a particular intermediate representation for observed time-series data is chosen, a bi-gram model is chosen as the linguistic resources to generate a text. However, there are several ways of describing a human motion, some people might describe a motion with 10 words, the other people might describe the motion with 15 words. Considering this, we introduce “null” label into the bi-gram model so that the most likely sentence can be generated without depending on the lenght of a sentence. The “null” labels are treated as the same as words in a setence, in other words, each of them has uni-gram and bi-gram as well as the other words. To deal with the “null” labels in that way, the following pre-processing for each sentence is required before applying dynamic programming to the bi-gram model – first, we obtain the maximum and minimum length of sentences. Secondly, we obtain the value of subtracting the minimum number from the maximum number, which corresponds to the maximum number of “null” labels used in the sentence with the minimum length. Thirdly, if a sentence is not the one with the maximum length, “null” labels with a number are inserted in decsending order from the end of the sentence toward the beginning of the sentence. Figure 6 shows an image of introducing “null” labels in sentences.
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A Scale Invariant Human Motion Detection System using Wavelet Based Feature Extraction

A Scale Invariant Human Motion Detection System using Wavelet Based Feature Extraction

Thresholding for image segmentation is generally made based on information contained in a gray level histogram of a given image. The aim of this approach is to identify the bottom of the histogram that positively separates the two groups or sub divisions. However, whether a pixel is an edge pixel or not depends not only on the gray values of the pixel, but also on gray values of the surrounding pixels. After approximating the gradient vector, one should not use the magnitudes of the derivatives alone for defining the appropriateness of a pixel to be an edge pixel, though it has been done in that way with many edge detectors. Threshold values may be selected based on the gray levels in the neighborhood of pixels which may vary from image to image. So, the changes in the neighborhood of a pixel also should be used in this analysis. In order to automatically vary the threshold normalization of gradient magnitudes is to be done with respect to the neighboring pixels gradient magnitude, and then it is to be confirmed whether the obtained value is large or not. A normal way of doing such normalization in any process is to use suitable statistical principles. This method of normalizing the gradient strength at each pixel locally before thresholding results in the elimination of the uncertainty, and thereby produces consistent, strong and smooth edges. The variance in the image is estimated by the equation 1.
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Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse

Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse

Based on this data, as a first step we developed a graph-matching approach for referential ground- ing (Liu et al., 2012; Liu et al., 2013). This ap- proach uses Attributed Relational Graph to cap- ture collaborative discourse and employs a state- space search algorithm to find proper ground- ing results. Although it has made meaning- ful progress in addressing collaborative referen- tial grounding under mismatched perceptions, the state-space search based approach has two ma- jor limitations. First, it is neither flexible to ob- tain multiple grounding hypotheses, nor flexible to incorporate different hypotheses incrementally for follow-up grounding. Second, the search al- gorithm tends to have a high time complexity for optimal solutions. Thus, the previous approach is not ideal for collaborative and incremental di- alogue systems that interact with human users in real time.
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Human Motion Detection in Video Surveillance using Computer Vision Technique

Human Motion Detection in Video Surveillance using Computer Vision Technique

Object detection is usually achieved by object detectors or background subtraction. An object detector is often a classifier that scans the image by a sliding window and labels each sub image defined by the window as either object or background. Generally, the classifier is built by offline learning on separate datasets or by online learning initialized with a manually labeled frame at the start of a video. Alternatively, background subtraction compares images with a background model and detects the changes as objects. It usually assumes that no object appears in images when building the background model. Such requirements of training examples for object or background modeling actually limit the applicability of above-mentioned methods in automated video analysis Another category of object detection methods that can avoid training phases are motion- based methods which only use motion information to separate objects from the background. Given a sequence of images in which foreground objects are present and moving differently from the background, can we separate the objects from the background automatically. The goal is to take the image sequence as input and directly output a mask sequence. The most natural way for motion-based object detection is to classify pixels according to motion patterns, which is usually named motion segmentation. These approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. However, they assume rigid motion or smooth motion in respective regions, which is not generally true in practice. In practice, the foreground motion can be very complicated with nonrigid shape changes. Also, the background may be complex, including illumination changes and varying textures such as waving trees and sea waves. The video includes an operating escalator, but it should be regarded as background for human tracking purpose. An alternative motion-based approach is background estimation. Different from background subtraction, it estimates a background model directly from the testing sequence. Generally, it tries to seek temporal intervals inside which the pixel intensity is unchanged and uses image data from such intervals for background estimation. However, this approach also relies on the assumption of static background. Hence, it is difficult to handle the scenarios with complex background or moving cameras.
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A Modern Approach for Motion Detection and Responsive Control of Appliance using MATLAB

A Modern Approach for Motion Detection and Responsive Control of Appliance using MATLAB

Motion tracking is a major issue in security field whether it is borders, banks, offices and institutions etc. Security is always maximum concerned. To maintain security we deploy security guards but with them human errors are most common as they cannot available on a place all the time. Hardware sensor based systems are very costly and maximum lasts for few years only.it can be placed on single place. This paper proposes to create motion detection system using software. It deals with the concept of motion tracking using cameras in real time. It is designed to create a visitor identification system in which motion is detected MATLAB system reads predefined message.
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An improved scheme for detection and labeling in Johansson displays

An improved scheme for detection and labeling in Johansson displays

Human motion analysis is a very important and hard problem in computer vision. When observing social interactions that take place in the surrounding environment, humans are, in general, the most important component. The interest is further justi- fied by the number of application for which understanding people’s actions and inten- tions is a central step. Among them, for instance, is monitoring people in airports or museums for security reasons. Detection of pedestrians is attractive to the automotive industry for safety and autonomous navigation systems. Even the daily interaction with computers and appliances could be greatly improved by a more user-friendly interface (in a sense, a more passive one, where it’s the machine to autonomously infer what we expect it to do).
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A Modern Approach for Motion Detection and Responsive Control of Appliance Using MATLAB

A Modern Approach for Motion Detection and Responsive Control of Appliance Using MATLAB

ABSTRACT: Motion tracking is a major issue in security field whether it is borders, banks, offices and institutions etc. Security is always maximum concerned. To maintain security we deploy security guards but with them human errors are most common as they cannot available on a place all the time. Hardware sensor based systems are very costly and maximum lasts for few years only. it can be placed on single place. This paper proposes to create motion detection system using software. It deals with the concept of motion tracking using cameras in real time. It is designed to create a visitor identification system in which motion is detected MATLAB system reads predefined message.
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Morphological-Edge Detection Approach for the Human Iris Segmentation

Morphological-Edge Detection Approach for the Human Iris Segmentation

From the beginning, the grayscale format (Rakshit and Monro, 2007) is performed in the captured mages. Then, the holes (the hole is the region which dim pixels surrounded light pixels in the image) that exist in the gray level images must be identified. After that, in order to make an edge map on this gray level image, Canny edge detection operator (Othman et al., 2019) which is an effective method is utilized. Subsequently, morphological operators (de Mira et al., 2015; Ahmadi and Akbarizadeh, 2016) are used to eliminate small objects. Following that, the pupil and limbic boundary are detected morphologically. Afterwards, the reflections of the pupil are removed by creating a mask. Later, the connected component labeling (Solomatin et al., 2018) on binary images is carried out. Succeeding that, in order to omit the noise, the least-squares fit of ellipse to 2D points (Mulleti and Seelamantula, 2015) is done, and pupil and limbic boundary are detected. And finally, the iris area is discovered (see Fig. 3 and Fig. 4).
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Human Motion Analysis via Statistical Motion Processing and Sequential Change Detection

Human Motion Analysis via Statistical Motion Processing and Sequential Change Detection

In this work, a novel approach for the analysis of human motion in video is presented. The kurtosis of interframe illumination variations leads to binary masks, the Activity Areas, which indicate which pixels are active throughout the video. The temporal evolution of the activities is character- ized by temporally weighted versions of the Activity Areas, the Activity History Areas. Changes in the activity taking place are detected via sequential change detection, applied on the interframe illumination variations. This separates the video into sequences containing different activities, based on changes in their motion. The activity taking place in each subsequence is then characterized by the shape of its Activity Area or on its magnitude and direction, derived from the Activity History Area. For nontranslational activities, Fourier Shape Descriptors represent the shape of each Activity Area, and are compared with each other, for recognition. Translational motions are characterized based on their relative magnitude and direction, which are retrieved from their Activity History Areas. The combined use of the aforementioned recognition techniques with the proposed sequential change detection for the separation of the video in sequences containing separate activities leads to successful recognition results at a low computational cost. Future work includes the development of more sophisticated and complex recognition methods, so as to achieve even better recognition rates. The application of change detection on video is also to be extended to a wider range of videos, as it is a generally applicable method, not limited to the domain of human actions.
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Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

Alternatively, background subtraction can be carried out by comparing the current frame against the first frame from the continuous video sequence. Given that there is no object in this initial frame, the problem mentioned above could be avoided. We can now acquire the whole moving object regardless of its moving speed. However, the biggest flaw of this approach could render the whole approach useless. Consider a situation where there is a vehicle in the first initial frame, and then it is gone. It will cause the algorithm to always detect motion at the place where the vehicle initially appears. This flaw can be reduced by continuously renewing the initial frame after a certain interval of time, but still, there is no guarantee that the newly obtained initial frame only contains a static background.
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An automated approach for detection of diabetic Retinopathy in human Eye

An automated approach for detection of diabetic Retinopathy in human Eye

[2] Agrawal, Ankit, Charul Bhatnagar, and Anand Singh Jalal. "A survey on automated microaneurysmdetection in Diabetic Retinopathy retinal images." InInformation Systems and Computer Networks(ISCON), 2013 International Conference on, pp. 24-29. IEEE, 2013. [3] Kamel, Mohamed, et al. "A neural network approach for the automatic detection ofmicroaneurysms in retinal angiograms."Neural Networks, 2001. Proceedings. IJCNN'01.International Joint Conference on. Vol. 4. IEEE, 2001.

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DNA LABELING, HYBRIDIZATION, AND DETECTION (Non-Radioactive)

DNA LABELING, HYBRIDIZATION, AND DETECTION (Non-Radioactive)

radioactivity is used, autoradiography using X-ray film is employed to visualize the hybrid positions. When chemically labeled probes are used, colorimetric reactions are most often use[r]

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DEVELOPMENT OF MOTION BASED MULTIPLE HUMAN DETECTION AND TRACKING USING BACKGROUND SUBTRACTION ALGORITHM

DEVELOPMENT OF MOTION BASED MULTIPLE HUMAN DETECTION AND TRACKING USING BACKGROUND SUBTRACTION ALGORITHM

Based on the results of the methods above, height of the motion region will get detected by adopting the method of combining horizontal with vertical projection. This can eliminate the impact of the shadow to a certain level. Then we analyze the vertical projection value and set the threshold value to remove the pseudo-local maximum value and the pseudo-local minimum value of the vertical projection to determine the number and width of the body in the motion region, we will get the moving human body with precise edge. This paper assumes that people in the scene are all in upright-walking state.
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