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Visual attribute detction for pedestrian detection

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Abstract

Attributes are expected to narrow down the semantic gap between low-level visual features and high-level semantic meanings. Such superiority motivates us to explore pedestrian attributes which has became a critical problem to boost image understanding and improve the performance of pedestrian detection, retrieval, re-identification, etc. Based on the PETA dateset, we manually relabel two subset VIPeR and PRID as our experimental dataset. Moreover, we proposed an evaluation protocol for researchers to evaluate pedestrian attribute classification algorithms. In this paper, we utilized two baseline methods to to demonstrate the performance of the attribute in pedestrian detection. The first one directly uses color and texture features to train Support Vector Machine (SVM) classification while the other one uses DSIFT (Dense SIFT) with Bag-of-Visual-Words (BoVW) to train SVM classification. Finally, we report and discuss the baseline performance on the database following the proposed evaluation protocol.

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Notes

  1. The Y channel of YCbCr is selected as the luminance channel.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61502337, 61472275, 61170239, 61303208), the Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC162000), and the grant of Elite Scholar Program of Tianjin University (2014XRG-0046).

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Authors and Affiliations

  1. School of Electronic Information Engineering, Tianjin University, Tianjin, China

    Jing Zhang, Fu-Wu Li, Wei-Zhi Nie, Wen-Hui Li & Yu-Ting Su

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  1. Jing Zhang
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  2. Fu-Wu Li
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Correspondence to Wei-Zhi Nie.

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Zhang, J., Li, FW., Nie, WZ. et al. Visual attribute detction for pedestrian detection. Multimed Tools Appl 78, 26833–26850 (2019). https://doi.org/10.1007/s11042-016-4258-5

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