Vision based moving object tracking through enhanced color image segmentation using Haar classifiers

Mahalingam, T. V.; Mahalakshmi, M.

doi:10.1109/tisc.2010.5714650

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Then we need to extract features from it. For this, haar features [16,17].For each feature calculation, they introduced the integral images. It simplifies calculation of sum of pixels.…”

Section: Proposed Methods a Face Detectionmentioning

confidence: 99%

Smart Reconnaisance and Apprise System using Raspberry PI

Alwar¹

2018

IJRASET

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In day today life, the security of our belonging is becoming more important. The modern surveillance is integrated with many automation technologies. In this modern world, crime has become ultra modern tool. At this point of time a lot of incident occurs like robbery, stealing unwanted entrance happens, threatening abruptly. So the security does matters in this daily life. This paper presents the monitoring and controlling of surveillance system enhanced with WI-FI (IEEE 802.11) technology. This system consists of webcam, Raspberry PI (BCM2837).In this system, webcam is used to detect the motions or to trace out the intruders and also detect fire. In case of any human movement or fire accident occurs, the webcam will capture live data in the surroundings and transmit it to authorized user through WI-FI(IEEE 802.11). Simultaneously, the buzzer alerts the nearby neighbors. The system also consist of sprayer, sprays the chloroform liquid on the intruders.

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“…Then we need to extract features from it. For this, haar features [16,17].For each feature calculation, they introduced the integral images. It simplifies calculation of sum of pixels.…”

Section: Proposed Methods a Face Detectionmentioning

confidence: 99%

Smart Reconnaisance and Apprise System using Raspberry PI

Alwar¹

2018

IJRASET

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“…To study the similarity between data from different eye trackers, experiments or comparison of data from a test experiment with a reference dataset, certain data similarity metrics based on histograms can be used. Such metrics for histogram comparison include correlation [ 45 ], intersection [ 46 ] and Bhattacharya distance [ 47 ].…”

Section: Deriving Evaluation Metrics For Eye Tracking Devices and mentioning

confidence: 99%

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations

Kar

Corcoran

2018

Sensors

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An eye tracker’s accuracy and system behavior play critical roles in determining the reliability and usability of eye gaze data obtained from them. However, in contemporary eye gaze research, there exists a lot of ambiguity in the definitions of gaze estimation accuracy parameters and lack of well-defined methods for evaluating the performance of eye tracking systems. In this paper, a set of fully defined evaluation metrics are therefore developed and presented for complete performance characterization of generic commercial eye trackers, when they operate under varying conditions on desktop or mobile platforms. In addition, some useful visualization methods are implemented, which will help in studying the performance and data quality of eye trackers irrespective of their design principles and application areas. Also the concept of a graphical user interface software named GazeVisual v1.1 is proposed that would integrate all these methods and enable general users to effortlessly access the described metrics, generate visualizations and extract valuable information from their own gaze datasets. We intend to present these tools as open resources in future to the eye gaze research community for use and further advancement, as a contribution towards standardization of gaze research outputs and analysis.

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“…Chowdary [3] showed us an example to implement image processing algorithm for gesture recognition. The work of T. Mahalingam [4] in vision based moving object tracking through enhanced color image segmentation using Haar classifiers is very helpful to understand the underlying principle of color tracking which is also this project"s backbone.…”

Section: Introductionmentioning

confidence: 99%

Image Processing Based Customized Image Editor and Gesture Controlled Embedded Robot Coupled with Voice Control Features

Kar¹,

Jana²,

Chatterjee³

et al. 2015

ijacsa

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Abstract-In modern sciences and technologies, images gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data) like microarray data in genetic research, or real-time multi-asset portfolio trading in finance etc. In this paper, a proposal has been presented to implement a Graphical User Interface (GUI) consisting of various MATLAB functions related to image processing and using the same to create a basic image processing editor having different features like, viewing the red, green and blue components of a color image separately, color detection and various other features like noise addition and removal, edge detection, cropping, resizing, rotation, histogram adjust, brightness control that is used in a basic image editor along with object detection and tracking. This has been further extended to provide reliable and a more natural technique for the user to navigate a robot in the natural environment using gestures based on color tracking. Additionally, Voice control technique has been employed to navigate the robot in various directions in the Cartesian plane employing normal Speech recognition techniques available in Microsoft Visual Basic.

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Vision based moving object tracking through enhanced color image segmentation using Haar classifiers

Cited by 10 publications

References 10 publications

Smart Reconnaisance and Apprise System using Raspberry PI

Smart Reconnaisance and Apprise System using Raspberry PI

Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations

Image Processing Based Customized Image Editor and Gesture Controlled Embedded Robot Coupled with Voice Control Features

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