Attention-Aided Generative Semantic Coding for Privacy-Preserving Traffic Status Monitoring

Traffic status monitoring is essential to support intelligent transportation, and demands for highly efficient and secure processing and transmission of massive data. Inspired by recent information-theoretic study, the traffic status monitoring is formulated as a privacy-preserving semantic coding problem in this work to characterize the fundamental trade-off among data compression, traffic image reconstruction, segmentation of traffic participants, and preservation of pixel regions of traffic participants. Grounded on the theoretic results, a novel generative semantic coding scheme is further developed for data-driven semantic coding design with the aid of attention mechanisms to capture correlations between sparse information. Experiments on the RCooper dataset demonstrate the effectiveness of the proposed generative semantic coding scheme and its advantages over the benchmark compression methods.