Temporal Pooling in Inflated 3DCNN for Weakly-supervised Video Anomaly Detection

Anomaly detection in surveillance videos requires significant attention in feature engineering to discriminate anomaly activity patterns from normal patterns. Keeping this in mind, this paper aims to extract superior quality spatio-temporal features from Inflated 3DCNN followed by a temporal pooling strategy to intensify relevant spatio-temporal feature in untrimmed anomalous videos. A superior temporal pooling strategy leads to better understanding of temporal dependency through LSTM model, which has become a necessary step for anomaly detection in surveillance videos. Thus, we propose a method consisting of an ideal temporal pooling strategy in inflated 3DCNN feature map along with LSTM model for temporal dependency encoding for weakly-supervised anomaly detection task. Our method is validated on a large scale video anomaly detection dataset, namely UCF-crime, resulting competitive performance in anomaly detection task with recent state-of-the-art methodologies.