Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring
- Publisher:
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Type:
- Journal Article
- Citation:
- IEEE Internet of Things Journal, 2023, 10, (5), pp. 3735-3748
- Issue Date:
- 2023-03-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Human_Inertial_Thinking_Strategy_A_Novel_Fuzzy_Reasoning_Mechanism_for_IoT-Assisted_Visual_Monitoring.pdf | Published version | 7.12 MB |
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Computer vision has always been a hot field of research by contemporary scholars due to its wide range of applications. As an important branch of this field, the visual monitoring technology has shown superior vitality in the actual monitoring environment of the Internet of Things (IoT). However, when the monitoring environment is complex, once the target monitoring fails, the important information related to the target also disappears. At this time, if the existing monitoring method is used, the target cannot be monitored again. Moreover, the current filtering monitoring algorithm also has the problem of poor interpretability. Therefore, this article combines the relevant characteristics of human inertial thinking when dealing with such problems. First, our method screens the movement information of the target and introduces a fuzzy reasoning mechanism to infer the location area of the target through fuzzy thinking. Then, an alternative selection strategy based on the thinking set is applied, which alternates between the location of thinking reasoning and the location of memory to further obtain the effective visual monitoring of the target. The filtering and monitoring algorithm fused with the new mechanism in the OTB-2015 data set, the UVA123 data set, and the TC128 data set all show that the proposed fuzzy inference mechanism has good robustness and universality. Furthermore, our results confirm that it can not only ensure the monitoring speed and overall accuracy but also improve the stability of monitoring in the IoT-assisted monitoring environment, showing its effectiveness compared to state-of-the-art methods. In addition, our results confirm that the integration of the proposed edge learning method with the IoT can be well applied to the construction of smart cities and future generation systems.
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