HPILN: A feature learning framework for cross-modality person re-identification
This addresses the problem of identifying individuals across different camera types in video surveillance systems, representing an incremental improvement over existing methods.
The paper tackles cross-modality person re-identification between RGB and infrared images by proposing HPILN, a feature learning framework that modifies existing single-modality models and uses hard pentaplet and identity losses, achieving state-of-the-art performance on the SYSU-MM01 dataset in terms of CMC and MAP metrics.
Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera views, light brightness, etc. To meet these challenges a novel feature learning framework, HPILN, is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to improve the performance of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, which show that the proposed method outperforms all existing methods in terms of Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP).