CBFD: a refined W4+ cluster-based frame difference approach for efficient moving object detection
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Nowadays, automated object detection and tracking is needed for video observation, robotic control, and vehicle driver aid systems. Object detection and tracking is an essential, complex activity in PC system vision due to the challenges in tracking. Continual contortion of objects at that time of motion and background scatter causes inadequate tracking. Non-static objects can be efficiently identified by deviation between the frames and clustering techniques, yet background system need to be upgraded consistently, and it is also vulnerable to camera jitter and lighting variants. In order to overcome these issues, an effective cluster-based frame differencing and W4+ method is proposed. These methods are helping to efficiently recognize the non-static object, in which a single technique cannot provide an efficient result. The proposed refined W4+ incorporated with CBFD method is used to detect the efficient moving object detection during the image analysis process. In addition to that, the fuzzy morphological filter processing to reduce the noise drastically. The objects with various sizes, contours, and lighting variants are available in the dataset, which makes the assessment of data a difficult task. The speculative outcomes and efficiency analysis on real video series datasets illustrate the efficiency of our strategies in contrast with existing strategies.
KeywordsNon-static object detection Background subtraction Frame distinction Cluster-based frame difference (CBFD) Threshold Morphology W4+
TM contributed to technical and conceptual content, architectural design. MS contributed to guidance and counselling on the writing of the paper.
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Conflict of interest
The authors declare that they have no conflict of interest.
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There is no animal involvement in this research.
The authors declare that they have no consent.
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