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Auflistung nach Schlagwort "Gait and Action Recognition"

1 - 2 von 2
  • Konferenzbeitrag
    Cyclist Recognition from a Silhouette Set
    (BIOSIG 2023, 2023) Eijiro Makishima, Fumito Shinmura
    Person recognition from surveillance cameras can be useful for criminal investigations. Currently, gait recognition technology can identify walking individuals, but recognition of people riding bicycles has not been actively investigated, despite cycling being a popular mode of transportation. In this paper, we propose a method to recognize individuals riding bicycles (cyclists) using a silhouette set. We captured two types of cyclist data, normal and rush modes, from five different views, and generated silhouette image sequences from this data. We evaluated accuracy of the proposed method on the silhouette images in identification and verification tasks. The evaluation results demonstrate the effectiveness of our proposed method.
  • Konferenzbeitrag
    Human-centered evaluation of anomalous events detection in crowded environments
    (BIOSIG 2023, 2023) Giulia Orrù, Elia Porcedda
    Anomaly detection in crowd analysis refers to the ability to detect events and people’s behaviours that deviate from normality. Anomaly detection techniques are developed to support human operators in various monitoring and investigation activities. So far, the anomaly detectors' performance evaluation derives from the rate of correctly classified individual frames, according to the labels given by the annotator. This evaluation does not make the system's performance appreciable, especially from a human operator viewpoint. In this paper, we propose a novel evaluation approach called ``Trigger-Level evaluation'' that is shown to be human-centered and closer to the user's perception of the system's performance. In particular, we define two new performance metrics to aid the evaluation of the usability of anomaly detectors in real-time.