Top PDF Real time Detecting System of High Knee Movement Based on Kinect

Real time Detecting System of High Knee Movement Based on Kinect

Real time Detecting System of High Knee Movement Based on Kinect

The latest version of Kinect SDK provided a skeleton tracking tool, which can collect joint information in the form of points. And this information was collected in key frames. In each frame, the tool estimated and collected the location of 20 points. When the skeleton was tracked by the Kinect, the first feature that can be magnified is the position of the joint. Because each joint has three coordinate values, each of which consists of 20 joints. Therefore, the eigenvector has 60 dimensions. When dealing with pre-defined gestures, we can choose important joints to represent these gestures. As shown in Figure 2, we observe that 10 joints (A, B, C, D, E, F, G, H, S and Q) are the most important joints that representing high knee movements. By collecting the information of these joints, different angles can be calculated, which can be used as a basis for determining the action of raising the knees.
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Mouseless- Camera Based Mouse by Detecting Real-Time Face

Mouseless- Camera Based Mouse by Detecting Real-Time Face

ABSTRACT: Today lots of projects based on hand free movement of mouse cursor are available in market but somehow they have some limitations, someone may lack the speed of mouse cursor or require some color code to identify the objects. Our objective is to gain or access data from machine at real time environment and converting that information into productive output in the form of movement of mouse pointer with high accuracy.

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

that the proposed method has a good performance in the detection of people, but in some cases these results were not accurate in detecting of a human body when he moves in a similar color environment. In this case, the color of the body is similar to the color of background . Or if the moving man was away from the camera (the distance between the person and the camera is too far). The researchers suggested in [16] another method to detect the moving human and to follow him through building model by using human walking cycle which is called HGPLVM (hierarchical Gaussian process latent variable model), they built a multiple models for the movement of a human in different positions , then they compared these models with the detected moving bodies, to determine if it was similar to the pedestrian. The results of this experiment showed good performance, but it has suffered from false positive cases many times , it may pick up some counterfeit objects .Researchers presented in [3] a model with many training samples, these samples were classified using (Support vector machine) SVM[7]. It classifies offered samples to return a number of these types, as the possible object might be one of them. Then the types that have got the highest number of votes are further classified using a another classification depending on Cascade [8] that is used to choose the best one among the selected objects. Tests’ results have shown a high percentage of success of identifying objects. Still, problems encountered in the last research were the relative high implementation time, as a result of classifying many models; in addition to, the need for a large number of training samples to be trained regularly. As for [4] researchers submitted their models in real-time, relying on Average of Synthetic Exact Filters (ASEF) [9]. This is among the fast algorithms in implementation time; it depends on conducting some mathematical processes over the object to be compared with previously stored models. Based on such calculations it can know whether the object is identical to one of such models or not The problem is that it is greatly sensitive to lighting and disruption , and also used filter ASEF, is not efficient enough on changing the angle of the camera view, and its inability to handle several moving objects simultaneously in different sizes in the same image and at the same time.
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A Robust Environmental Sound Recognition System using Frequency Domain Features

A Robust Environmental Sound Recognition System using Frequency Domain Features

This work consider the task of recognizing environment sounds for the understanding of a scene (or context) surrounding an audio sensor. Here auditory scenes refer to a location with different acoustic characteristics such as a coffee shop, park or quiet hallway. Consider, for example, applications in robotic navigation and obstacle detection, assistive robots, surveillance, and other mobile device based services. Many of these systems are dominantly vision-based. When being employed to understand unstructured environments, their robustness or utility will be lost if visual information is compromised or totally absent. Audio data could be easily acquired, in spite of challenging external
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A Novel Method for Detecting Drowsiness In Real Time

A Novel Method for Detecting Drowsiness In Real Time

ABSTRACT--- Driver’s drowsiness poses a major threat to roadway safety and can lead to critical physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. In this paper, we proposed an algorithm to determine the level of fatigue by measuring the eye opening and closing, and warn the driver accordingly.

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A Real Time Virtual Dressing Room  Application using Kinect and HD camera

A Real Time Virtual Dressing Room Application using Kinect and HD camera

Trying clothes in clothing stores is usually a time consuming activity. Moreover, it might not even be possible to try on clothes in the store, such as when ordering clothes online. Here we propose a simple virtual dressing room application to make shopping for clothing faster, easier, and more accessible. The first problem we address in the design of our application is the accurate superimposition of the user and virtual cloth models. Detection and skeletal tracking of a user in a video stream can be implemented in several ways. For example, Kjaerside et al. [1] proposed a tag-based augmented reality dressing room, which required sticking visual tags for motion tracking. More recently, Shotton et al. [2] have developed a real-time human pose recognition system that predicts the 3D positions of body joints, using a single depth image without visual tags. In this project, we use Shotton et al.’s method and a Microsoft Kinect sensor to create a tagless, real-time augmented reality dressing room application. Developer tools, such as the ones included in the OpenNI framework and the Microsoft Kinect SDK, ease developing applications based on the Kinect sensor. We used the Kinect SDK as it includes a robust real-time skeletal body tracker based on [2].
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Detecting harassment in real time as conversations develop

Detecting harassment in real time as conversations develop

We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or mut- ing or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.
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Social Media Crowdsourcing and Detecting Financial Fraud Using Real Time System

Social Media Crowdsourcing and Detecting Financial Fraud Using Real Time System

Crowdsourcing is a process of getting workdone through a group of people i.e. crowd using internet. The basic idea behind Crowdsourcing is to take the work and give it to a group of workers where any member of the group completes the work and get paid his/her efforts. The key advantage is that using crowdsourcing a firm assigns its task to a wide range of skilled people who have the ability to complete the task within short time span and much reduced cost as compared to the work done by the

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Real-time Human Detecting System Design for Intelligent Air-conditioning Control

Real-time Human Detecting System Design for Intelligent Air-conditioning Control

The applications of air conditioners on the market is quite popular, involving the most basic control technologies and functions are quite mature. However, there is a serious lack of use in human design, such as: 1. The air conditioner is still in the working state, though there are nobody in the rooms; 2. The flow directions of air conditioner cannot change with the position of human, etc, which results in a large number of energy waste. The most common intelligent air conditioner system is usually controlled by infrared sensors, which will sense the current environmental temperature so as to control the state of air conditioner. However, the obvious shortcoming is that the infrared sensor cannot detect and locate human.
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Real-time Implementation of Character Movement by Floating Hologram based on Depth Video

Real-time Implementation of Character Movement by Floating Hologram based on Depth Video

First, it checks if Kinect is connected. The Unreal Engine creates the character model that has the human structures. Kinect capture the depth information and analyze it. When a person is detected in depth information, it extracts the skeleton information from the depth information. It divides the depth information into the head, the shoulder, the arm, the leg, and so on and connect each part into the character model. The character model is converted into the floating hologram by the Unreal Engine. The hologram is projected onto the floating hologram device. The flowcharts of this processing is shown in Figure 8.
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

reflections which helps in increasing spectral efficiency, impedance and axial bandwidth of RF system. One more advantage of circularly polarized antennas is that they communicate better even with linear polarized antennas for some specific applications.

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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Though research on shadow detection and removal has been going on for more than two decades, high accuracy is still not achieved for complex scenes making it a ongoing research topic. Polidorio proposed a method in which the difference between saturation and intensity is thresholded in the HSI color space [1]. Huang used the Phong illumination model and thresholded the hue and green –blue difference and the blue component [2]. Tsai in his method considered the various color models like HSI, YC b C r , HCV, HSV and YIQ. He thresholded
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

4.4.5. Influence of External Systems on the Quality of Financial Information External systems and a significant positive effect on the quality of financial information, thus the external system directly affects the quality of the financial information is significant. External system has indicators hardware and software indicators get the lowest loading factor is the hardware and which has a loading factor is the ultimate software with a high level of significance means that when the software is always carried out up to date latest version, performed maintenance and database systems in East Java Private Polytechnic it will improve the quality of financial information. 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 impact on the quality of financial information private Polytechnic in East Java. The development of hardware and software have grown so quickly that the Polytechnic swsata in East Java must upgrade the hardware and software up to date so that clicking the quality of financial information generated will be more qualified. Thus the results of this study support the research [6], with the title of Key Issues of Accounting Information Quality Management: Australian Case Study and the findings of the external factors is a critical factor that determines the quality of accounting information. In addition, this study does not support the research [13], Analysis of factors affecting the quality of local government financial information (Empirical Study of Semarang regency and municipal government),
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DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

DEVELOPING A HIGH PERFORMANCE REAL TIME PEOPLE DETECTION SYSTEM

Hierarchical Agglomerative Clustering [14] enhances the constraints based clustering. It employs clustering constraints ordering format to improve the similarity of constraints in a hierarchical manner. Predicting priority via a multi- faceted factor analysis automatically prioritizes the bug reports that depend on machine learning. The priority level focuses multiple factors such as temporal, textual, related-report, author, product, and the severity of the bug reports. The extracted features of various factors are the input for ThresholdinG and Linear Regression to ClAssifY Imbalanced Data (GRAY) classification engine [15]. Graph partitioning algorithm [16] clusters the textual information of bug reports. It exploits three clustering algorithms such as normalized cut (summary), size regularized cut (description), and k-means algorithm (summary and description).
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Driver Fatigue Detection System and the Status Transmission

Driver Fatigue Detection System and the Status Transmission

ABSTRACT: This paper describes the methods of detecting the early signs of fatigue/drowsiness while driving. By identifying and analyzing the various parameters and variables, the detection the loss of alertness prior to driver falling asleep is possible. From the response of this technique one can detect that the Loco-Pilot is able to drive or not. And the status can be transferred online to the base station to take the safety measures in case of emergency. A hardware system has been developed to acquire variables, as well as an algorithm to detect fatigue via image processing taking into account the other aspects. Mainly this project presents hardware cum software technique for fatigue detection in train driver and transmitting the status via GSM.
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Prevention of Session Hijacking and IP Spoofing With Sensor Nodes and Cryptographic Approach

Prevention of Session Hijacking and IP Spoofing With Sensor Nodes and Cryptographic Approach

1.Kiruthiga.S and Yuvarani.G in year 2012 proposed a technique for the fast and accurate detection of fake access point using non-cryptomethod in which they calculate the clock skew of an AP from the IEEE 802.11 Time Synchronization. Function (TSF) time stamps sent out in the beacon/probe response frames. They uses two different methods for this purpose—one based on linear programming and the other based on least-square fit. They supplement these methods with a heuristic for differentiating original packets from those sent by the fake APs. They collect TSF time stamp data from several APs in three different residential settings. Using their measurement data as well as data obtained from a large Setting, they find that clock skews remain consistent over time for the same AP but vary significantly across APs..
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Prevention of Session Hijacking and Ipspoofing with Sensor Nodes and Cryptographic Approach

Prevention of Session Hijacking and Ipspoofing with Sensor Nodes and Cryptographic Approach

In this technique the MAC address spoofing is possible so to prevent against the MAC spoofing new enhancement in the proposed mythology has been proposed. In this technique the public and private key cryptography is used to prevent MAC spoofing. At the start when the sensor nodes storing the knowledge about each node like MAC address, coverage area, attached devices, etc in the network at that time it assign a unique key to each access point in the network to prove its identity. And stores the unique key in their databases corresponding to each AP configuration that have saved. When a access point starts communication it send the unique key encrypt with the sensor node’s public key to the sensor node. Sensor node decrypt with its private key and verifies from there database that the key is assign to this MAC address if yes positive acknowledgement will send to the access point if not matched it will send the alarm message to the wireless device about the fake access point. This message exchange is encrypted and decrypted which the asymmetric encryption algorithm. Below flowchart of the proposed technique is given
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Real Time 3D Reconstruction Using a Kinect Sensor

Real Time 3D Reconstruction Using a Kinect Sensor

After implementing the algorithm, a frame rate of 8-10 fps was obtained. Compared with the requirements of KinectFusion, it’s a low rate, but, fortunately, KinFu approach permitted it for model scanning. The total processing time was 23 minutes. The final point cloud at a 0.5 mm resolution contains approximately 4.5 million points and the resulted mesh 1.5 million polygons.

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A Survey on Traffic Monitoring System for Detection And Tracking Vehicles at Night Time

A Survey on Traffic Monitoring System for Detection And Tracking Vehicles at Night Time

This presents an approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low- level image processing modules and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatio-temporal analysis during day time, and by morphological analysis of headlights at night. The high-level module is designed as a forward chaining production rule system, working on symbolic data, i.e., vehicles and their attributes and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness [14].
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Vehicle Detector Method for Complex Environments on Embedded Linux Platform

Vehicle Detector Method for Complex Environments on Embedded Linux Platform

The first stage of the project, the videos are captured using a fixed camera. Video cameras are standard equipment for modern transportation and management. Traffic surveillance is important to update the information about the movement of traffic. The camera is located at high spot as shown in figure2, it is positioned in such a way that vehicles are coming towards the camera in order to avoid the side views of movement of vehicles and overlapping of vehicles.

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