Multiple human object tracking using background subtraction and shadow removal techniques

Saravanakumar, S.; Vadivel, A.; Ahmed, Christine

doi:10.1109/icsip.2010.5697446

Cited by 43 publications

(16 citation statements)

References 16 publications

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“…V. OBJECT TRACKING METHODS Tracking can be defined as the problem of approximating the path of an object in the image plane as it moves around a scene. The purpose of tracking object is to generate the path to an object above time finding its position in every single frame of film [5]. Object tracked object extraction, object recognition and tracking, and decisions on activities.…”

Section: Object Classification Methodsmentioning

confidence: 99%

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A survey study on detecting and tracking objective methods

Ramasubramanian

Rangaswamy

Reddy

2014

2014 IEEE National Conference on Emerging Trends in New &Amp; Renewable Energy Sources and Energy Management (NCET NRES EM)

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The purpose of prosecution is segmentation feature a region of interest in a video scene and retention proposal, positioning and locking. Object detection and classification of objects are preceded steps for tracking an object in the image sequence. Object detection is done to check for objects in the video and find out exactly what the object. Then the detected object can be classified into different categories, such as people, vehicles, birds, clouds floating, swaying trees and other moving objects. Object Tracking is performed using spatial and temporal changes of items during a video monitoring, including presence, position, size, shape, etc. The object is used in several applications such as video surveillance, robot vision, monitoring traffic Video and Animation painting. This paper presents a brief overview of object detection, object classification and object tracking different algorithms available in the literature, including the analysis and comparative study of the various techniques used at various stages of prosecution.

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Section: Object Classification Methodsmentioning

confidence: 99%

“…The algorithm works efficiently in case of occlusion in video sequences. In [5] a tracking algorithm based on adaptive background subtraction on video -detecting and tracking moving objects is presented in this paper. First, the average filter is used to real images background video denoise and videos.…”

Section: Object Trackingmentioning

confidence: 99%

A survey study on detecting and tracking objective methods

Ramasubramanian

Rangaswamy

Reddy

2014

2014 IEEE National Conference on Emerging Trends in New &Amp; Renewable Energy Sources and Energy Management (NCET NRES EM)

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“…The convergence of these algorithms depends on the representation, the number of object tracked and the method used for approximating the object motion. We can cite the most used techniques that use this approach Simple Template Matching [30], The Mean Shift Method [3], the Support Vector Machine [25], and the Layering based tracking [37]. (iii) Silhouette Tracking Methods: in the silhouette tracking methods, the tracking is performed by using the information encoded within the segmented object region.…”

Section: Related Workmentioning

confidence: 99%

Evidential Deformable Model for Contour Tracking. Application on Brain Cine MR Sequences

Naffakhi¹,

Nakib²,

Hamouda³

2017

Metaheuristics for Medicine and Biology

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The goal of this paper is to introduce an efficient evidential particle filter for complex shapes tracking. The particularity of that particle filter is not only the fair use of the observation at the current time in the update step of it by performing a curve evolution but also it represents a bridge between Probability function and Evidence theory. This bridge can be illustrated by incorporating a data fusion step in the update phase. This method builds a track by selecting the best particles between the particle candidates. This re-sampling phase is based on choosing the particles possessing the higher value of the basic belief assignment function. The values of these basic belief assignment functions are resulting from the fusion process of two distinctive sources of information. The first source is the energy functional and the second one is the local sensitive histogram. The evaluation of our approach, which is made on a realistic Brain cine RM sequences, aims at tracking the motion of the walls of the third ventricle. Therefore, the latter shows its obvious and clear efficiency. In order to validate our proposal, we present a comparative study between our proposal and the state of the art methods. The obtained results are encouraging.

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“…The challenges in a robust tracking algorithm are caused by the presence of noise, occlusion, background clutter, varying viewpoints and illumination changes. Recently, a few of algorithms are proposed to overcome these difficulties (Yimaz et al, 2006;Saravanakumar et al, 2010;Meng et al, 2009). In a tracking video sequence, an unknown target should be defined as anything which is interesting to analysis.…”

Section: Introductionmentioning

confidence: 99%

Object Tracking Approach based on Mean Shift Algorithm

Xiao-jing¹,

Yue²,

Sha³

2013

JMM

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Object tracking has always been a hotspot in the field of computer vision, which has a range of applications in real world. The object tracking is a critical task in many vision applications. The main steps in video analysis are: detection of interesting moving objects and tracking of such objects from frame to frame. Most of tracking algorithms use pre-defined methods to process. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences. Experimental results show that the Mean shift tracker is effective and robust tracking method

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Multiple human object tracking using background subtraction and shadow removal techniques

Cited by 43 publications

References 16 publications

A survey study on detecting and tracking objective methods

A survey study on detecting and tracking objective methods

Evidential Deformable Model for Contour Tracking. Application on Brain Cine MR Sequences

Object Tracking Approach based on Mean Shift Algorithm

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