  Video-based person re-identification (Re-ID) aims at matching the video
tracklets with cropped video frames for identifying the pedestrians under
different cameras. However, there exists severe spatial and temporal
misalignment for those cropped tracklets due to the imperfect detection and
tracking results generated with obsolete methods. To address this issue, we
present a simple re-Detect and Link (DL) module which can effectively reduce
those unexpected noise through applying the deep learning-based detection and
tracking on the cropped tracklets. Furthermore, we introduce an improved model
called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical
Non-local Network, we replace the non-local module with three 1-D
position-sensitive axial attentions, in addition to our proposed coarse-to-fine
structure. With the developed CF-AAN, compared to the original non-local
operation, we can not only significantly reduce the computation cost but also
obtain the state-of-the-art performance (91.3% in rank-1 and 86.5% in mAP) on
the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for
data alignment, to our surprise, several baseline models can achieve better or
comparable results with the current state-of-the-arts. Besides, we discover the
errors not only for the identity labels of tracklets but also for the
evaluation protocol for the test data of MARS. We hope that our work can help
the community for the further development of invariant representation without
the hassle of the spatial and temporal alignment and dataset noise. The code,
corrected labels, evaluation protocol, and the aligned data will be available
at https://github.com/jackie840129/CF-AAN.
