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

Reference MARS Notes
"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 [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 + 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


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[7] Z. Zhong, L. Zheng, D. Cao, and S. Li. Re-ranking Person Re-identification with k-reciprocal Encoding. In CVPR 2017