Table 1 The rank-1 precious (%) and mAP (%) on the Market-1501 database
From: Learning deep features from body and parts for person re-identification in camera networks
Method | Single | Query | Multi | Query |
|---|---|---|---|---|
| Â | rank-1 | mAP | rank-1 | mAP |
DADM [26] | 39.40 | 19.6 | 49.00 | 25.8 |
BoW+KISSME [19] | 44.42 | 20.26 | – | – |
MST-CNN [27] | 45.1 | – | 55.40 | – |
MR-CNN [28] | 45.58 | 26.11 | 56.59 | 32.26 |
FisherNet [29] | 48.15 | 29.94 | – | – |
CAN [30] | 48.24 | 24.43 | – | – |
SL [31] | 51.90 | 26.11 | 56.59 | 32.26 |
SCSP [31] | 51.90 | 26.35 | – | – |
DNS [32] | 55.43 | 29.87 | 71.56 | 46.03 |
S-LSTM [33] | – | – | 61.60 | 35.3 |
Gate Reid [34] | 65.88 | 39.55 | 76.04 | 48.45 |
SOMAnet [35] | 73.87 | 47.89 | 81.29 | 56.98 |
MSCAN [36] | 75.45 | 52.41 | 83.43 | 62.03 |
PIE [37] | 78.65 | 53.87 | – | – |
Verif.-Identif [16] | 79.51 | 59.87 | 85.84 | 70.33 |
Part-based features | 36.25 | 14.47 | 37.86 | 13.51 |
Body-based features | 80.29 | 59.34 | 86.81 | 71.18 |
DFBP | 81.71 | 60.86 | 87.02 | 72.21 |