Important Scene Detection Based on Anomaly Detection using Long Short-Term Memory for Baseball Highlight Generation
Kaito Hirasawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2020
This paper presents an important scene detection method based on anomaly detection using a Long Short-Term Memory (LSTM) for baseball highlight generation. In order to deal with multi-view time series features calculated from tweets and videos, we adopt an anomaly detection method using LSTM. LSTM which can maintain a long-term memory is effective for training such features. Introduction of LSTM into important scene detection of baseball videos is the biggest contribution of this paper. Experimental results show high detection performance by our method.