Bidirectional Convolutional LSTM for the Detection of Violence in Videos

Abstract

The field of action recognition has gained tremendous traction in recent years. A subset of this, detection of violent activity in videos, is of great importance, particularly in unmanned surveillance or crowd footage videos. In this work, we explore this problem on three standard benchmarks widely used for violence detection: the Hockey Fights, Movies, and Violent Flows datasets. To this end, we introduce a Spatiotemporal Encoder, built on the Bidirectional Convolutional LSTM (BiConvLSTM) architecture. The addition of bidirectional temporal encodings and an elementwise max pooling of these encodings in the Spatiotemporal Encoder is novel in the field of violence detection. This addition is motivated by a desire to derive better video representations via leveraging long-range information in both temporal directions of the video. We find that the Spatiotemporal network is comparable in performance with existing methods for all of the above datasets. A simplified version of this network, the Spatial Encoder is sufficient to match state-of-the-art performance on the Hockey Fights and Movies datasets. However, on the Violent Flows dataset, the Spatiotemporal Encoder outperforms the Spatial Encoder.