The 2020 IEEE/RSJ International Conference on Intelligent Robots (IROS2020) is now held October 25 – November 25 at on-demand conference. The following oral presentation will be delivered.

Sensor-based activity recognition for construction vehicles is useful for evaluating the skills of the operator, measuring work efficiency, and many other use cases. Therefore, many researches have explored robust activity-recognition models. However, it remains a challenge to apply the model to many construction sites because of the imbalance of the dataset.
While it is natural to employ multi-label representation on imbalanced data with a large number of activity categories, multi-label robust classification for activity recognition has yet to be resolved because of the nature of the time-series property.

In this work, we propose a novel multi-label long short-term memory (LSTM) model, which is effective for the sequence multi-labeling problem. The proposed model has connections to the temporal direction and attribute direction, which exploit both the temporal pattern and co-occurrence among attributes. In addition, by providing a bidirectional connection structure in the attribute direction, the model enables us to alleviate the dependency of the chain order in what we call classifier chain, which is a classical approach to multi-label classification. To validate our methods, we conduct experiments using real-world construction-vehicle dataset.

-presentation information-
Haruka Abe*, Takuya Hino**, Motohide Sugihara**, Hiroki Ikeya**, and Shimosaka, Masamichi*: Multi-Label Long Short-Term Memory for Construction Vehicle Activity Recognition with Imbalanced Supervision.
(*Tokyo Institute of Technology, **Komatsu Ltd.)

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