Thursday, 2023/03/30

  • CS/AI
  • C
  • TITECH
  • Switch Language
    • ja日本語 (Japanese)
    • enEnglish

Shimosaka Research Group

pursuing MIUBIQ (machine intelligence in UbiComp Research)

  • Home
    • Members
    • Location
  • News
  • Projects
  • Publications
  • Awards
  • Archives
    • Codes
    • Datasets
Navigation
News Presenting our paper on neural-based time-series multi-label classification problem towards reliable activity recognition on construction vehicles at IROS2020

Presenting our paper on neural-based time-series multi-label classification problem towards reliable activity recognition on construction vehicles at IROS2020

2020/10/26 | NewsPresentations | 796 views |

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.)

NOTE: You could register IROS2020 on-demand for free via

  • tweet

Comments are disabled for this post

Social Networks

  • twitter
  • rss

Recent News

  • Device-Free Multi-Person Indoor Localization Using the Change of ToF 2023/03/03
  • Presenting our paper on Device-Free Multi-Person Indoor Localization Using the Change of ToF at PerCom2023 2023/02/28
  • Our paper on Efficient Adaptive Beacon Deployment Optimization for Indoor Crowd Monitoring has been published in IMWUT. 2023/01/24
  • Presenting our paper on Robust Continuous MaxEnt IRL with RRT at IV2022 2022/06/09
  • Presenting our paper on Efficient Indoor Localization Model Construction by Sequential Recommendation of Data Gathering Position based on Bayesian Optimization at IPIN2021 2021/11/29
  • Adaptive incremental beacon placement optimization for crowd density monitoring applications 2021/11/01
  • Presenting 2 papers at ACM SIGSPATIAL 2021 2021/11/01
  • Fine-grained Urban Dynamics Prediction using Large-Scale Mobile Phone Location Data 2021/10/05
  • Robustifying Wi-Fi localization by Between-Location data augmentation 2021/09/28
  • Our paper on robustifying Wi-Fi localization by “Between-Location” data augmentation has been published in IEEE Sensors Journal 2021/09/17

Search

Copyright 2015 · Shimosaka Research Group at TITECH