2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IVASR-4.3
Paper Title POSITIONAL ENCODING: IMPROVING CLASS-IMBALANCED MOTORCYCLE HELMET USE CLASSIFICATION
Authors Hanhe Lin, Guangan Chen, University of Konstanz, Germany; Felix Siebert, Friedrich-Schiller University of Jena, Germany
SessionMLR-APPL-IVASR-4: Machine learning for image and video analysis, synthesis, and retrieval 4
LocationArea B
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent advances in the automated detection of motorcycle riders' helmet use have enabled road safety actors to process large scale video data efficiently and with high accuracy. To distinguish drivers from passengers in helmet use, the most straightforward way is to train a multi-class classifier, where each class corresponds to a specific combination of rider position and individual riders' helmet use. However, such strategy results in long-tailed data distribution, with critically low class samples for a number of uncommon classes. In this paper, we propose a novel approach to address this limitation. Let n be the maximum number of riders a motorcycle can hold, we encode the helmet use on a motorcycle as a vector with 2n bits, where the first n bits denote if the encoded positions have riders, and the latter n bits denote if the rider in the corresponding position wears a helmet. With the novel helmet use encoding, we propose a deep learning model that stands on existing image classification architecture. The model simultaneously trains 2n binary classifiers, which allows more balanced samples for training. This method is simple to implement and requires no hyperparameter tuning. Experimental results demonstrate our approach outperforms the state-of-the-art approaches by 1.9% accuracy.