TY - GEN
T1 - Fused discriminative metric learning for low resolution pedestrian detection
AU - Li, Xinzhao
AU - Liu, Yuehu
AU - Chen, Zeqi
AU - Zhou, Jiahuan
AU - Wu, Ying
N1 - Funding Information:
The authors would like to thank all the experts who have contributed to this paper. This work is supported by National Natural Science Foundation of China (NSFC) under Grant No.91520301 and Grant No.61328303. What's more, this work was supported in part by National Science Foundation grant IIS-1217302, IIS-1619078, and the Army Research Ofice ARO W911NF-16-1-0138.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - Low resolution (LR) is one of the most challenging factor in pedestrian detection. In this paper, we propose a fused discriminative metric learning (F-DML) approach for low resolution pedestrian detection without explicit super resolution. We firstly learn a discriminative high resolution (HR) feature space as target space. Then, an optimal Mahanalobis metric is learned to transform the LR feature space into a new LR classification space, which largely preserves the discriminative structure of the HR feature space. Finally, a weighted K-nearest neighbors classifier is applied in the LR classification space which inherits good discrimination from HR feature space. A new training strategy is proposed to find the fewest and most representative LR-HR exemplars. In addition, we build a new dataset for the evaluation of low resolution pedestrian detection methods. Extensive experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods.
AB - Low resolution (LR) is one of the most challenging factor in pedestrian detection. In this paper, we propose a fused discriminative metric learning (F-DML) approach for low resolution pedestrian detection without explicit super resolution. We firstly learn a discriminative high resolution (HR) feature space as target space. Then, an optimal Mahanalobis metric is learned to transform the LR feature space into a new LR classification space, which largely preserves the discriminative structure of the HR feature space. Finally, a weighted K-nearest neighbors classifier is applied in the LR classification space which inherits good discrimination from HR feature space. A new training strategy is proposed to find the fewest and most representative LR-HR exemplars. In addition, we build a new dataset for the evaluation of low resolution pedestrian detection methods. Extensive experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods.
KW - Low resolution
KW - Metric learning
KW - Pedestrian detection
UR - http://www.scopus.com/inward/record.url?scp=85062918785&partnerID=8YFLogxK
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U2 - 10.1109/ICIP.2018.8451791
DO - 10.1109/ICIP.2018.8451791
M3 - Conference contribution
AN - SCOPUS:85062918785
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 958
EP - 962
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
T2 - 25th IEEE International Conference on Image Processing, ICIP 2018
Y2 - 7 October 2018 through 10 October 2018
ER -