Abstract
Effective and accurate detection of clothing categories in streets is imperative for the description and analysis of crowd clothing profile which is crucial for fashion designers and E-commerce. In this paper, we present a real-time tracking system from surveillance videos to detect and track the various clothing categories with a state-of-the-art deep learning approach, which proposes a combinational framework based on deep convolutional neural network (CNN). First, we take advantage of the mechanism of Focal Loss to improve the loss function of the one-stage detector YOLOv2. We adopt CREST as the visual tracker which largely overcomes the difficulties of occlusion and deformation for clothing detection. Additionally, we have collected and preprocessed a dataset including over 1,200,000 still images from previous works and thousands of video fragments for the training and validation of CNNs. Finally, we compare our work with the baseline and previous work, and our framework demonstrates its effectiveness and accuracy.
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This work is supported by WTU SF-165003/165010 and FRFCU SCUN-CZQ18008.
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Huang, J., Wu, X., Zhu, J., He, R. (2019). Real-Time Clothing Detection with Convolutional Neural Network. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_28
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DOI: https://doi.org/10.1007/978-981-10-8944-2_28
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