Abstract
Moving object detection is a crucial and critical task for any surveillance system. Conventionally, a moving object detection task is performed on the basis of consecutive frame difference or background models which are based on some mathematical aspects or probabilistic approaches. But, these approaches are based on some initial conditions and short amount of time is needed to learn all these models. Also, the bottleneck in all these previous approaches is that they require neat and clean background or need to create a background first by using some approaches and that it is essential to update them regularly to cope with the illuminating changes. In this paper, moving object detection is executed using visual attention where there is no need for background formulation and updates as it is background independent. Many bottom-up approaches and one combination of bottom-up and top-down approaches are proposed in the present paper. The proposed approaches seem more efficient due to inessential requirement of learning background model and due to being independent of previous video frames. Results indicate that the proposed approach works even against slight movements in the background and in various outdoor conditions.
Highlights
The process of detecting a moving object has a great significance for computer vision field like video surveillance, automatic target detection and tracking, and so forth
Static image sequence is used for reducing complexity of algorithm but applying the same on each image frame of video would not be a proper solution for object detection
Method name Modified frequency tuned spatial model I Visual saliency based on colour image Gaussian spatial model by coalescing Laplacian of Gaussian (LoG) filter and Gaussian filter Gradient saliency by utilizing Sobel operator Modified frequency tuned spatial model II Integrating colour saliency with texture feature (local binary pattern (LBP)) Integrating modified frequency tuned spatial model I with histogram of oriented gradient (HOG) Gaussian mixture model (GMM) Kernel density estimation (KDE)
Summary
The process of detecting a moving object has a great significance for computer vision field like video surveillance, automatic target detection and tracking, and so forth. Capturing the same scene at various times and detecting the changes in them has a large amount of applications in diverse fields Real time applications such as robotics visions and surveillance would use series of frames as excitation and the problem of object detection can be considered for such systems as moving objects detection. An approach based on the above conditions uses the most efficient algorithms for static images which would be slower than that of required moving object detection techniques for real time analysis. These task dependent factors include context and feature based cues amongst others.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
