Contrastive Learning Based Dynamic Redundancy Detection for Visual Crowdsensing Data

Visual Crowdsensing (VCS) has gradually become an emerging research field as the built-in cameras of smart mobile devices have become a common recording tool in daily life. To meet the task requirements of VCS applications in the sensing process, sensing platforms usually use distributed data acquisition to collect image data from different sources. However, this leads to a large amount of redundant data in the final collected data set, which seriously affects the data quality. To solve the above problems, this paper proposes a dynamic redundancy detection method (VCSRD) for visual crowdsensing data based on contrastive learning. The method fuses the multimodal information of metadata and visual content through comparative clustering to realize the redundancy detection of data. Not only improves the accuracy of redundant data detection but also has some flexibility for data under different tasks. The effectiveness and flexibility of the proposed method under different data sizes are verified through experiments on real image datasets. Compared to the baseline method, VCSRD shows superior performance on all three clustering metrics.