An Overview of Text-Based Person Search: Recent Advances and Future Directions

Due to the practical significance in smart video surveillance systems, Text-Based Person Search (TBPS) has been one of the research hotspots recently, which refers to searching for the interested pedestrian images given natural language sentences. To help researchers quickly grasp the developments of this important task, we comprehensively summarize the recent research advances of TBPS from two perspectives, i.e., Feature Extraction (FE) and Semantic Alignments (SA). Specifically, the FE mainly consists of pre-processing approaches and end-to-end frameworks, and the SA could be briefly divided into cross-modal attention mechanism, non-attention alignments, training objectives, and generative approaches. Afterwards, we elaborate four widely-used benchmarks and also the evaluation criterion for TBPS. And comparisons and analyses among the state-of-the-art (SOTA) solutions are provided based on these large-scale benchmarks. At last, we point out some future research directions that need to be further addressed, which will greatly facilitate the practical applications of TBPS.