Real-time object tracking using color-based probability matching

Jansari, Deval; Parmar, Shankar; Saha, Geetali

doi:10.1109/ispcc.2013.6663399

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…This section briefly describes the existing systems for object detection and tracking in videos and the techniques used by their authors for the same. Jansari et.al [1] proposed a system for detection based mobile object tracking using color probability features. The methodology consists of two different detection techniques namely background subtraction and optical flow based detection executed against video inputs containing multiple mobile objects with occlusions taken into account.…”

Section: Related Workmentioning

confidence: 99%

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Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Pillai,

2019

IJEAT

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Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.

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Section: Related Workmentioning

confidence: 99%

“…Few of the researches involve tracking of objects by means of detection, wherein the mobile object is continuously detected in frames and labeled. One such algorithm makes use of color histogram probability and centroid features for object detection [1].…”

Section: Introductionmentioning

confidence: 99%

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Pillai,

2019

IJEAT

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“…Besides, it only requires few frames for processing and no preprocessing task is required. However, this method generally has high computational complexity and thus making it to be unsuitable for real-time algorithms [8]. The second method is the background subtraction where works in a way where the moving object is detected by examining the difference of the pixel feature of the current image against the reference image [9].…”

Section: Motion Detectionmentioning

confidence: 99%

An Application on Medical Tele-Diagnosis Robot (MTR) for Real-Time Motion Detection

Mariappan

Nadarajan

Porle

et al. 2014

AMM

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The use of medical robots in healthcare industry especially in rural areas are hitting limelight these days. Development of Medical Tele-diagnosis Robot (MTR) has gain importance to unravel the need of medical emergencies. Nevertheless, challenges for a better visual communication still arises. Thus, a face identification and tracking system for MTR is designed to allow an automated visual which will ease the medical specialist to identify and keep the patient in the best view for visual communication. This paper emphasis on the motion detection module which is the first module of the system. An improved motion detection technique is proposed which suits a real-time application for a dynamic background. Frame differencing method was used to detect the motion of the target. The developed motion detection module succeeded an accuracy of 96% resulting an average of 97% of the whole MTR.

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Real time objects detection and positioning in multiple regions using single fixed camera view for video surveillance applications

Aradhya¹,

Mohana²,

Chikodi³

2015

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

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Real-time object tracking using color-based probability matching

Cited by 7 publications

References 6 publications

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

An Application on Medical Tele-Diagnosis Robot (MTR) for Real-Time Motion Detection

Real time objects detection and positioning in multiple regions using single fixed camera view for video surveillance applications

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