Robust skin color-based moving object detection for video surveillance

Kalirajan Kaliraj; Sudha Manimaran

doi:10.1117/1.JEI.25.4.043007

14 July 2016 Robust skin color-based moving object detection for video surveillance

Kalirajan Kaliraj, Sudha Manimaran

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043007 (July 2016). https://doi.org/10.1117/1.JEI.25.4.043007

Abstract

Robust skin color-based moving object detection for video surveillance is proposed. The objective of the proposed algorithm is to detect and track the target under complex situations. The proposed framework comprises four stages, which include preprocessing, skin color-based feature detection, feature classification, and target localization and tracking. In the preprocessing stage, the input image frame is smoothed using averaging filter and transformed into YCrCb color space. In skin color detection, skin color regions are detected using Otsu’s method of global thresholding. In the feature classification, histograms of both skin and nonskin regions are constructed and the features are classified into foregrounds and backgrounds based on Bayesian skin color classifier. The foreground skin regions are localized by a connected component labeling process. Finally, the localized foreground skin regions are confirmed as a target by verifying the region properties, and nontarget regions are rejected using the Euler method. At last, the target is tracked by enclosing the bounding box around the target region in all video frames. The experiment was conducted on various publicly available data sets and the performance was evaluated with baseline methods. It evidently shows that the proposed algorithm works well against slowly varying illumination, target rotations, scaling, fast, and abrupt motion changes.

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Kalirajan Kaliraj and Sudha Manimaran "Robust skin color-based moving object detection for video surveillance," Journal of Electronic Imaging 25(4), 043007 (14 July 2016). https://doi.org/10.1117/1.JEI.25.4.043007

Published: 14 July 2016

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