Abstract
People’s attire, or their way of dressing defines not only their social status and personality but also affects the way people meet and greet them. Attire detection has many useful applications such as clothing preferences in diverse regions of the world could be monitored and quantified. This information is very valuable for fashion designers. Real-time clothing recognition can be useful for security surveillance, where information about an individual’s clothes can be used to identify crime suspects. Recently, deep learning algorithms have shown promise in the field of object detection and recognition. These algorithms are data hungry and are only as good as the data they are trained on. In this work, we have focused on three tasks to address this problem. We created a unique dataset of ~8000 images from IMDBb.com (movie rating website) to address the challenge of real-world application of the algorithm training for attire detection. The dataset contains pictures from movies, making the dataset a good source of images from the wild. We manually labelled 60 different classes of attire. Then we focused on multiclass classification and attire object detection using customized deep learning architectures including YOLO and SSD. We achieved a mean Average Precision (mAP) of 64.14% and an Average Precision (AP) of 91.14% for top 5 classes on YOLO. Available at https://github.com/saadyousuf45/.
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Yousuf, S.B., Sajid, H., Poon, S., Khushi, M. (2019). IMDB-Attire: A Novel Dataset for Attire Detection and Localization. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_46
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