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Conference Paper
Intra-Batch Features Separation for Indoor and Outdoor Pedestrian-View Intersection Classification
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- Issue Date
- 2021-10
- Citation
- International Conference on Control, Automation and Systems (ICCAS) 2021, pp.1-4
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Pedestrian-view intersection classification is an important component to assist robots to navigate in the pedestrian path. To solve this problem, previous approaches simply fine-tune an ImageNet-pretrained network with intersection classification dataset using cross-entropy loss as classification loss in an end-to-end manner. In this work, we propose a novel additional loss to further improve the model's capability to discriminate intersection and non-intersection class. This loss is directly calculated on the features in a given mini-batch without requiring any additional inference. Furthermore, previous works cover only outdoor domain while we also propose indoor domain in addition to the outdoor intersection classification dataset. Extensive experiments show that the models trained using the proposed loss yields better performance compared to the models trained without it on both indoor and outdoor datasets. This demonstrates the potential of the proposed loss in improving the discrimination capability of our models.
- KSP Keywords
- Additional loss, Cross entropy, End to End(E2E), Entropy loss