Robust Pedestrian Attribute Recognition for an Unbalanced Dataset using Mini-batch Training with Rarity Rate
- Hiroshi Fukui, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi, Hiroshi Murase
- IEEE Intelligent Vehicle Symposium, 2016
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Pedestrian attributes are significant information for Advanced Driver Assistance System(ADAS). Pedestrian attributes such as body poses, face orientations and open
umbrella are meant action or state of pedestrian. In general, this information is recognized using independent classifiers for each task. Performing all of these separate tasks is too timeconsuming at the testing stage. In addition, the processing time increases with the number of tasks. To address this problem, multi-task learning or heterogeneous learning is able to train a single classifier to perform multiple tasks. In particular, heterogeneous learning is able to simultaneously train regression and recognition tasks, because reducing both training and testing time. However, heterogeneous learning tends to result in a lower accuracy rate for classes with a few training samples. In this paper, we propose a method to improve the performance of heterogeneous learning for such classes. We introduce a rarity rate based on the importance and class probability of each task. The appropriate rarity rate is assigned to each training sample. Thus, the samples in a mini-batch for training a deep convolutional neural network are augmented by this rarity rate to focus on the class with a few samples. Our heterogeneous learning approach with the rarity rate attains better performance on pedestrian attribute recognition, especially for classes representing open umbrellas.