A Duplex Spatiotemporal Filtering Network for Video-based Person Re-identification

Video-based person re-identification plays important roles in surveillance video analysis. This paper proposes a duplex spatiotemporal filtering network (DSFN) to re-identify persons in videos. A video sequence is represented as a duplex spatiotemporal matrix. DSFN model containing a group of filters performs filtering at feature levels in both temporal and spatial dimensions, by which the model focuses on feature-level semantic information rather than image-level information as in the traditional filters. We propose sparse-orthogonal constraints to enforce the model to extract more discriminative features. DSFN seeks to characterize not only the appearance features but also dynamic information such as gaits embedded in video sequences and obtains performance improvement as a result. Experimental results show that the proposed method outperforms other comparison approaches.