Robust object detection based on radial reach correlation and adaptive background estimation for real-time video surveillance systems

Abstract

A method of real-time object detection for video surveillance systems has been developed. The method aims to realize robust object detection by using Radial Reach Correlation (RRC). We also apply a statistical background estimation to cope with dynamic and complex environments. The computational cost of RRC is higher than the simple subtraction method and the background estimation method based on statistical approach needs large memory. It is necessary to reduce the calculation cost in order to apply to an embedded image processing device. Our method is composed of two techniques: fast RRC algorithm and background estimation based on statistical approach with cumulative averaging process. As a result, without deterioration in detection accuracy, the processing time of object detection can be decreased to about 1/4 in comparison with normal RRC.

On the Role and the Importance of Features for Background Modeling and Foreground Detection

“…-Statistical Features: Statistical features can be obtained by exploiting some of the statistical properties of the representation of the visual. The first work developed by Satoh et al [428] proposed a Radial Reach Correlation (RRC) feature which has several variants: Bi-polar Radial Reach Correlation (BP-RCC) [422], Fast Radial Reach Correlation (F-RRC) [218][219], and Probabilistic Bipolar Radial Reach Correlation (PrBP-RCC) [582]. In a similar way, Yokoi [580] used Peripheral Ternary Sign Correlation (PTESC).…”