People detection

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Pictorial Structures Revisited: People Detection and Articulated Pose Estimation

Pictorial Structures Revisited: People Detection and Articulated Pose Estimation

Both people detection and human pose estimation have a large variety of applications such as automotive safety, surveillance, and video indexing. The goal of this paper is to develop a generic model for human detection and pose es- timation that allows to detect upright people (i.e., pedestri- ans [12]), as well as highly articulated people (e.g., in sports scenes [15]), and to estimate their poses. Our model should also enable upper body detection and pose estimation [6], e.g., for movie indexing. The top performing methods for these three scenarios do currently not share the same archi- tecture, nor are components necessarily similar either. Here, we present a generic approach that allows for both human detection and pose estimation thereby addressing the above mentioned scenarios in a single framework. Due to its care- ful design the proposed approach outperforms recent work on three challenging datasets (see Fig. 1 for examples).
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People Detection and Recognition using Gait for Automated Visual Surveillance

People Detection and Recognition using Gait for Automated Visual Surveillance

We have proposed a new method to classify moving objects and recognize people for automated visual surveillance by their gait. Multiple objects are tracked successfully through the use of shape-based parameters to allocate them to different layers. Problems encountered during tracking such as background clutter, appearance of uninteresting objects and entry and exit of objects are handled efficiently. Finally moving regions are classified into either a single walking person, group of people or an undefined object such as vehicle. We have explored an alternative technique for walking people detection based on their gait motion. The experimental results confirm the robustness of our method to discriminate between moving objects with a detection rate of %100. For people recognition, a new model-based method is described to extract the joints positions via an evidence gathering technique. Spatial model templates for human motion are described in a parametrized form using the Fourier descriptor. The proposed solution has achieved a classification rate of %92 for people recognition. The model-based is suited to more generalized deplyment and this will be the focus for future work.
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An FPGA-Based People Detection System

An FPGA-Based People Detection System

Calculating the integral image directly from the JPEG co- efficients has the obvious advantage of eliminating the need for an explicit integrator. In fact, calculating the integral im- age directly is equivalent to decompressing the image. One might wonder why linear interpolation is used instead of simply storing a smaller image, since the images are essen- tially equivalent. Although a high-resolution image is not re- quired by this algorithm to detect people, the features will be misaligned at large scales unless they are placed at what is es- sentially subpixel resolution at small scales. The method that was chosen to achieve this was to duplicate pixels to allow more precise placement of features. Although this could have been achieved by fully decompressing a smaller image, it was evaluated that the bottleneck was more likely to be in stor- ing the image to memory rather than in receiving the com- pressed data. A tradeo ff can be achieved between the size of the input stream and the complexity of the on-chip decom- presser. This is due to the observation that JPEG decompres- sion does not scale linearly with the resolution. While a full- resolution decompression would require 64 accumulators, one for each pixel in the block, a (1 / 4)-resolution scan only requires 4 accumulators, or 1 / 16th of that needed for the full resolution. To give a feel for the amount of resources saved by this method, the module calculating the 4 exact points takes up 400 slices in a Virtex-II FPGA (each slice contains 2 flip-flops and 2 four-input lookup tables). The modules approximating the remaining 60 points take up collectively less than 100 slices. Even by limiting estimates to the storage space required for the DCT coefficients’ accumulators, cal- culating the exact values of the 60 remaining integral-image points would require more than taking 960 slices. This would have severe impacts on both placement and routing e ff orts for the entire module, possibly resulting in a reduced mini- mum period.
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

In this paper, we propose a robust multiwatermark embedding algorithm in DWT based on dynamic binary location by selecting a low frequency sub band from fifth level decomposition using t[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

In the code generation stage the language developer must produce the domain specific business meta-model, the specific service metamodel abstract syntax as an extension of our generic se[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

The proposed algorithm based on Hierarchical Task Network HTN enhances SHOP2 planning system to detect and generate a concurrent plan based on the output of SHOP2 sequence plan.. To trig[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Proposed approach provides better solution to cluster different overlay networks by using probabilistic k representative clustering algorithm and forms efficient summaries using phrase r[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Broker manager The proposed federated cloud model for ranking choose the top rank cloud service provider among the ranked cloud service providers and assign the the cloud service provide[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

The human factor can be considered as one of the most significant vulnerability; but unfortunately, it is often left unaddressed [8]. Organizations will not be able to protect the integrity, confidentiality, and availability of information assets if they ignore the human factor. In most organizations, managing information security threats focuses on managing technology and process, but little efforts are paid at managing people. A study by Ashenden [9] reaches that the human factor of information security management has largely been neglected. In fact, a small number of publications have actually addressed the human aspect of information security [9]–[11].
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

There are many methods used in the diagnosis of faults in power transformers, including traditional and intelligent .The use of an intelligent expert system relies on dissolved gas analy[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Shadow detection and removal has become very important in image processing. Satellite images contain shadows of various objects like buildings, trees, clouds, etc., which will hide the information of the underlying objects. The presence of shadows in images has both advantages and disadvantages. The shadows in images help in identifying the size and shape of the building which is useful for urban planning and reconstruction of scenes. Shadows also help to evaluate the size and the shape of buildings. The disadvantage of the presence of shadows is that they hide the information of the concealed objects, result in fake color tones, and distort the object’s shape.
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

All indicators on the external system gain above the required loading factor so good indicator of external systems hardware and software has a very strong influence and significant impac[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Fig.1 illustrates the bug report processing stages such as a pre-processing, meta-feature generation using bug ontology, BEME ontology-based prioritization for clustering, extended train[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

On the surface of the square patch circle, square and ring shaped slots are made to design the compact antenna for circular polarization radiation.. To excite symmetric diagonally placed[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

The research at automated surveillance systems aims to automate objects discovery and distinguish whether it is a target or not. Where it is possible that the moving object is human being or another organism like cats or dogs or etc. In the first case after distinguish it is a human being the system must be react by recognizing the character and a decision must be taken regarding it. Discovering people process usually related to a set algorithms and steps that is used to filters the received images and applying some image processing techniques like convert image to gray scale. Then in order to remove noisy and abnormal point, the predication of threshold to black and white colors converting is required. Next, some operations are applied by using filters and some techniques of image processing on related components which are called
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Keywords: Herrmann Whole Brain Model Learning Style HWBM LS, Behavioural Learning Patterns, Design Features of Web-Based Educational System WBES, Learner Modelling, Systematic Observatio[r]

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Dynamic semantic evolution of the embedded system thus can be achieved through Data Approach Invoking and deleting the existing Tasks as per the desired functionality, Rule based Approac[r]

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Redefining histograms of oriented gradients descriptors for handling occlusion in people detection

Redefining histograms of oriented gradients descriptors for handling occlusion in people detection

Object detection is a task with different issues regarding how the objects appear in images. First, there was the question of how to find/detect instances using their shapes (Borgefors, 1988); object were matched to a template that is rigid to any object variations, thus more convenient ways to describe object were required. Oren et al. (1997) proposed a detection framework that has influenced many nowadays detectors; they proposed an exhaustive approach for scanning images for any instance using sliding windows where Haar wavelet features are extracted then a support vector machine (machine learning) classifies whether instances are there or not. Viola and Jones (2001) introduced their face detection (a specific domain in object detection) framework and went successful for real-time performance thanks to the fast computing in Haar wavelets and the integral image.
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People Detection and Counting Using Fuzzy Color Histogram
SarithaPaturi & GVKS Prasad

People Detection and Counting Using Fuzzy Color Histogram SarithaPaturi & GVKS Prasad

The output of the change detection module is the bi- nary image that contains only two labels, i.e., ‘0’ and ‘255’, representing as ‘background’ and ‘foreground’ pixels respectively, with some noise. The goal of the connected component analysis is to detect the large sized connected foreground region or object which is one of the important operations in motion detection. The pixels that are jointly connected can be clustered into changing or moving objects by analyzing their con- nectivity.

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

[33] Juhaida Abdul Aziz, Parilah M.Shah and Rosseni Din 2014, “A Paradigm in Education: Validation of Web-Based Learning for Young Learner”, International Journal of Education and Inform[r]

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