Detection of Moving Objects

Dear Colleagues,

The detection of moving objects is one of the most important steps in the video processing field, such as in video-surveillance, optical motion capture, multimedia applications, teleconferencing, video editing, human-computer interface, etc. The last two decades have witnessed very significant publications on the detection of moving objects in video taken by static cameras; however, recently, new applications in which backgrounds are not static, such as recordings taken from drones, UAVs or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds and illumination changes in real scenes with fixed cameras or mobile devices are needed and so different models need to be used such as advanced statistical models, fuzzy models, robust subspace learning models and deep learning models.

The intent of this Special Issue is to provide: 1) new approaches in detection of moving objects, 2) new strategies to improve foreground detection algorithms to tackle critical scenarios, such as dynamic backgrounds, illumination changes, night videos and low-frame rate videos, and 3) new adaptive and incremental algorithms to achieve real-time applications.

This Special Issue is primarily focused on the following topics; however, we encourage all submissions related to detection of moving objects in videos taken by a static or moving cameras:

• Background initialization
• Background subtraction
• Background modeling
• Foreground detection
• Feature selection
• Statistical, Fuzzy, and Dempster-shafer concepts for detection of moving objects
• Robust subspace learning models (RPCA, etc.).
• Deep learning models.
• HD camera, IR cameras, Light Field cameras, RGB-D cameras
• Drones, UAV’s
• Real-time implementations (GPU, FPGA, etc.)

Prof. Thierry Bouwmans
Guest Editor

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