A multi-balanced hybrid optimization technique to track objects using rough set theory

Shanmugapriya, K.; Malar, R. Suja Mani

doi:10.1007/s11760-016-0976-4

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Tracking moving is one of the most challenging computer vision tasks. Several methods [24] [1] [3] [23] were proposed to deal with object tracking. In our study of existing methods, we focused on the modern methods called online trackers.…”

Section: Related Workmentioning

confidence: 99%

A Method for Tracking Road Objects

Jarraya¹

2018

IJMA

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In this paper, we present a new road traffic monitoring approach for a highway control and management system called RoadGuard. This system copes with several challenges in this type of applications in order to count and track road objects robustly. It adopts a novel approach for tracking road objects, first, to determine continuously their positions on the road, and then it uses vehicle positions to estimate their trajectories. The trajectory analysis provides vital information to control and manage highway traffic. In this paper, our main contribution is a tracking method based on a coherent strategy where both region and object information are used to establish objects correspondence over time. Our method operates in two phases: a spatial analysis that uses a multilevel region descriptors matching in order to identify object interactions and particular object states; and a continuous temporal analysis applied to cope with track management issues. As demonstrated experimentally, the proposed method can detect, track and count road objects accurately in highway videos that include several constraints. In addition, it produces effective and stable road objects tracking.

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Section: Related Workmentioning

confidence: 99%

A Method for Tracking Road Objects

Jarraya¹

2018

IJMA

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“…Background model is used to analyze the difference between the incoming frames of the video stream and the scene background. The extracted foreground objects can be classified into any one of the categories such as human, animal, vehicle, etc., based on the shapes, colors and features [9] [11] [6].…”

Section: Introductionmentioning

confidence: 99%

novel object detection technique for videos with homogeneous background

Devakumar

Murugan

2022

ijhs

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Object detection and tracking in video frames play a major role in all the computer vision systems. Several computer vision systems use background subtraction method for detecting the objects. However, Background subtraction method often results in lack of accuracy during frame comparison. Also, it leads to increase in memory requirements and computational complexity while setting threshold. The new Local Mean Diamond Pattern (LMDP) is introduced here to overcome the existing issues by extracting the objects directly from the frames. The extracted objects are tracked using mean shift tracking algorithm. The experimental result shows that the LMDP outperforms the existing methods viz., Local Binary Pattern (LBP), Local Extrema Pattern (LEP) and Local Rhombus Pattern (LRP) in terms of accuracy.

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Effective moving object tracking using modified flower pollination algorithm for visible image sequences under complicated background

Ong

Kiong

et al. 2019

Applied Soft Computing

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A multi-balanced hybrid optimization technique to track objects using rough set theory

Cited by 4 publications

References 15 publications

A Method for Tracking Road Objects

A Method for Tracking Road Objects

novel object detection technique for videos with homogeneous background

Effective moving object tracking using modified flower pollination algorithm for visible image sequences under complicated background

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