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Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challenging background scenes. Numerous existing approaches used multiple features simultaneously to address the problem but did not consider any adaptive/dynamic weight factor to combine these feature spaces. Being inspired by these observations, we propose a background subtraction-based real-time moving object detection method, called DFC-D. This proposal determines an adaptive/dynamic weight factor to provide a weighted fusion of non-smoothing color/gray intensity and non-smoothing gradient magnitude. Moreover, the color-gradient background difference and segmentation noise are employed to modify thresholds and background samples. Our proposed solution achieves the best trade-off between detection accuracy and algorithmic complexity on the benchmark datasets while comparing with the state-of-the-art approaches.

DFC-D: A dynamic weight-based multiple features combination for real-time moving object detection