Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area

Pushpa, D; Sheshadri, H. S.

doi:10.5120/4855-7125

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

0

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Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

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Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

Ray

¹

,

Ghosh

²

,

Chatterjee

³

et al. 2016

Innovative Research in Attention Modeling and Computer Vision Applications

0

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This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

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Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

Ray

¹

,

Ghosh

²

,

Chatterjee

³

et al. 2018

Computer Vision

0

View full text Add to dashboard Cite

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

show abstract

Multiple Object Detection and Tracking in Cluttered Region with Rational Mobile Area

Cited by 4 publications

References 2 publications

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

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