Weakly Supervised Learning of a Classifier for Unusual Event Detection

In this paper, we present an automatic classification framework combining appearance based features and Hidden Markov Models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should therefore be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous Hidden Markov Models (CHMMs). The applied learning procedure is as follows: 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments ...

• Type: Journal
• Year: 2008
• Where: TIP
• Authors: Mark Jager, Christian Knoll, Fred A. Hamprecht
• Keywords: Error Events, Image Sequences, Sequences, TIP 2008