Authors : Pragya Mittal; Jesna Jixon; Ritika Palai; Mrudula Wani; Shamla Mantri

Volume/Issue : Volume 11 - 2026, Issue 6 - June

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Google Scholar : https://tinyurl.com/2xnpbfaw

Scribd : https://tinyurl.com/mmheuwej

DOI : https://doi.org/10.38124/ijisrt/26jun1098

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : It combines dual-channel edge detection (Canny and Sobel), density-adaptive morphological structuring element selection, watershed-based object separation, and a seven-criterion shape filter to detect and count objects in urban surveillance images. The proposed approach is evaluated and compared against two segmentation-based baseline methods [1][2], demonstrating the advantage of density-adaptive parameter selection over fixed-parameter classical schemes. A proof-of-concept evaluation on two real urban surveillance images yielded object counts of 27 and 24, with complete pipeline telemetry confirming the density-adaptive feature.

Keywords : Adaptive Morphological Refinement, Edge Detection, Object Counting, Smart City, Canny Operator, Sobel Gradient, Watershed Segmentation, Connected Components, MATLAB.

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