English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years, video surveillance systems for the purpose of security have been developed rapidly. More and more researches try to develop intelligent video surveillance systems to replace the traditional passive video surveillance systems (Hu et al., 2004) and (Jacobs & Pless, 2008). The intelligent video surveillance system can detect moving objects in the initial stage and subsequently process the functions such as object classification, object tracking, and object behaviors description. Detecting moving object is a very important aspect of computer vision and has a very wide range of surveillance applications. The accurate location of the moving object does not only provide a focus of attention for post-processing but also can reduce the redundant computation for the incorrect motion of the moving object. The successful moving object detection in a real surrounding environment is a difficult task, since there are many kinds of problems such as illumination changes, fake motion (Cheng & Chen, 2006), night detection (Huang, 2008), and Gaussian noise in the background (Gonzalez & Woods, 2001) that may lead to detect incorrect motion of the moving object. There are three typical approaches for motion detection (Hu et al., 2004), (Jacobs & Pless, 2008), and (Collins, 2000): background subtraction, temporal differencing, and optical flow. The background subtraction method detects moving regions between the current frame and the reference background frame. It provides the most complete motion mask data, but is susceptible to dynamic scene changes due to lighting and extraneous events. Therefore, it has to update the reference background frame frequently. The temporal differencing approach extracts the moving region by using consecutive frames of the image sequences. It is suitable for dynamic environment, but often extracts incomplete relevant motion object pixels. The optical flow method uses characteristics of flow vectors of moving objects over time to detect moving regions. However, most optical flow methods are with higher complex computation. Generally, the above three moving object detection methods are all sensitive to illumination changes, noises, and fake motion such as moving leaves of trees.

An Improved Low Complexity Algorithm for 2-D Integer Lifting-Based Discrete Wavelet Transform Using Symmetric Mask-Based Scheme