Novel Region Growing Mechanism for Object Detection in a Complex Background

Sahoo, Tamanna; Mohanty, Bibhuprasad

doi:10.1007/978-981-15-5262-5_2

Cited by 1 publication

(1 citation statement)

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“…In both algorithms, along with the dynamics creation, traditional background subtraction has been utilized in the transformed domain. In the second method, wavelet coefficient features of image dynamics are combined selectively to obtain a seed-based region growing process [2] for detecting the moving object in the scene. The methods have been applied to different video sequences available in CDnet [3], SBMnet [4], AGVS [5], I2R [6], and UrbanTracker [7] dataset.…”

Section: Introductionmentioning

confidence: 99%

A Novel Region Growing Algorithm using Wavelet Coefficient Feature Combination of Image Dynamics

Sahoo¹,

Mohanty²

2022

IJACSA

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Moving object detection has versatile and potential applications in video surveillance, traffic monitoring, human motion capture etc., where detecting object(s) in a complex scene is vital. In the existing background subtraction method based on frame differencing, the false positive and misclassification rate increases as the background becomes more complex and also with the presence of multiple moving objects in the scene. In this piece of work, an approach has been made to enhance the detection performance of the background subtraction method by exploiting the dynamism available in the scene. The resultant differencing frame so obtained by the spatial background subtraction method is subjected to wavelet transformation. By extracting and combining wavelet features from the dynamics of the scene, a novel method of region growing technique has been further utilized to detect the moving object(s) in the scene. Simulation of various video sequences from CDnet, SBMnet, AGVS, I2R and Urban Tracker database has been applied and the method provides satisfactory detection of the moving object in a complex scene. The quantitative measure like Recall, Precision, F1-measure, and specificity computed for the algorithms, have indicated the algorithms can be a suitable candidate for surveillance applications.

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