Abstract
Moving object detection is a fundamental task on intelligent video surveillance systems, because it provides a focus of attention for further investigation. Thus, video object segmentation, which extracts the shape information of moving object from a video sequence, is a key operation for surveillance system. In this study, the current state-of-the-art in moving objects segmentation for intelligent video surveillance has been surveyed. An efficient modified directional lifting-based 9/7 discrete wavelet transform (MDLDWT) structure is proposed to further reduce the computational cost and preserve the fine shape information in low resolution image. Although perfect moving object detection in a practical environment is a challenging task due to the vague object shape issues in the low resolution configuration, the experimental results document that the proposed low-complexity MDLDWT scheme can provide more precise detection rate for multiple moving objects, and the fine shape information can be effectively preserved for the real-time video surveillance applications in both indoor and outdoor environments.
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