{
  "id": "1807.00612",
  "version": "v1",
  "published": "2018-07-02T12:04:24.000Z",
  "updated": "2018-07-02T12:04:24.000Z",
  "title": "Multi-modal Egocentric Activity Recognition using Audio-Visual Features",
  "authors": [
    "Mehmet Ali Arabacı",
    "Fatih Özkan",
    "Elif Surer",
    "Peter Jančovič",
    "Alptekin Temizel"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a \"supervector\", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-02T12:04:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multi-modal egocentric activity recognition",
      "egocentric activity recognition problem",
      "multi-modal features",
      "existing methods",
      "maximum a-posteriori adaptation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}