Top-Down and Bottom-Up Cues Based Moving Object Detection for Varied Background Video Sequences

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.