Abstract
The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.
Highlights
The term IoT (Internet of Things) was first used by Lee et al [1] defined IoT, called the Internet of Everything or the Industrial Internet, “is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other”
This paper focuses on comparison of various ChD algorithms suitable for resource constrained devices in VIoT
It can be seen that dataset No 11 stands out as it depicts extreme end of feature combination such as high object mobility, image source mobility, illumination changes, shadowing, fast change, variety of objects and of different sizes
Summary
The term IoT (Internet of Things) was first used by Lee et al [1] defined IoT, called the Internet of Everything or the Industrial Internet, “is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other”. DVS may be part of homogeneous VWSN (Visual Wireless Sensor Networks) [5] or VIoT containing heterogeneous devices with varying computational and storage capabilities. These devices may include resource constrained visual sensor nodes, with lower cost, smaller size, limited processing power, average quality cameras and smart phones cameras [6]. Image differencing and rationing are computationally inexpensive and can be implemented on low-form factor devices with minimal resources which makes these techniques well suited for VIoT and VWSN. Consider a typical VIoT based DVS system, where multiple VIoT devices such as visual sensor nodes, smart cameras acquire images and execute ChD algorithm.
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