State of the art on the MARS dataset

We summarize the state-of-the-art methods on the MARS dataset. We will report both mAP and rank-1, 5, 10, 20 accuracies. Note that this may not be the only performance measurement. Other metrics, such as recognition time, are also important. Please contact me at liangzheng06@gmail.com.

Reference MARS Notes
rank-1rank-5rank-20mAP
"MARS: A Video Benchmark for Large-Scale Person Re-identification", Liang Zheng, Zhi Bie, Yifan Sun, Jingdong Wang, Chi Su, Shengjin Wang, Qi Tian, ECCV 2016 2.66.412.40.8 HOG3D [1] + kissme [2], Euclidean distance, single query
1.22.87.40.4GEI [3] + kissme [2], single query.
18.633.045.98.0HistLBP [4] + XQDA [5], single query
30.646.259.215.5BoW [6] + kissme [2], single query
60.077.987.942.4IDE, average pooling, Euclidean distance, single query
65.081.188.945.6IDE + kissme, max pooling, Euclidean distance, single query
68.382.689.449.3IDE + kissme, max pooling, Euclidean distance, multiple query
Current state of the art
"Learning Compact Appearance Representation for Video-based Person Re-Identification", Wei Zhang, Shengnan Hu, Kan Liu, Arxiv 2017 55.5 70.2 80.2- A frame selection step is used before feature pooling
"Re-ranking Person Re-identification with k-reciprocal Encoding", Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li, CVPR 2017. 67.78 - -57.98IDE (CaffeNet) + re-ranking, single query.
73.94 - -68.45 IDE (ResNet50) + re-ranking, single query.
"In Defense of the Triplet Loss for Person Re-Identification", Alexander Hermans, Lucas Beyer and Bastian Leibe, Arxiv 2017. 79.80 91.36 -67.70 Using the fine-tuned TriNet and Euclidean distance, single query.
81.21 90.76 -77.43TriNet + re-ranking [7]
Use the dataset for training, but do not report results
"Simple Online and Realtime Tracking with a Deep Association Metric", Nicolai Wojke, Alex Bewley, Dietrich Paulus, ArXiv 2017. - - --The CNN model is trained on MARS

References

[1] Klaser, A., Marsza lek, M., Schmid, C.: A spatio-temporal descriptor based on 3dgradients. In: BMVC (2008).
[2] Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: CVPR. pp. 2288–2295 (2012) [3] Han, J., Bhanu, B.: Individual recognition using gait energy image. Pattern Analysis and Machine Intelligence, IEEE Transactions on 28(2), 316–322 (2006) [4] F. Xiong, M. Gou, O. Camps, and M. Sznaier. Person reidentification using kernel-based metric learning methods. In ECCV, 2014.
[5] S. Liao, Y. Hu, X. Zhu, and S. Z. Li. Person re-identification by local maximal occurrence representation and metric learning. In CVPR, 2015.
[6] Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person reidentification: A benchmark. In: CVPR (2015).
[7] Z. Zhong, L. Zheng, D. Cao, and S. Li. Re-ranking Person Re-identification with k-reciprocal Encoding. In CVPR 2017