  Abnormal event detection is a challenging task that requires effectively
handling intricate features of appearance and motion. In this paper, we present
an approach of detecting anomalies in videos by learning a novel LSTM based
self-contained network on normal dense optical flow. Due to their sigmoid
implementations, standard LSTM's forget gate is susceptible to overlooking and
dismissing relevant content in long sequence tasks like abnormality detection.
The forget gate mitigates participation of previous hidden state for
computation of cell state prioritizing current input. In addition, the
hyperbolic tangent activation of standard LSTMs sacrifices performance when a
network gets deeper. To tackle these two limitations, we introduce a bi-gated,
light LSTM cell by discarding the forget gate and introducing sigmoid
activation. Specifically, the LSTM architecture we come up with fully sustains
content from previous hidden state thereby enabling the trained model to be
robust and make context-independent decision during evaluation. Removing the
forget gate results in a simplified and undemanding LSTM cell with improved
performance effectiveness and computational efficiency. Empirical evaluations
show that the proposed bi-gated LSTM based network outperforms various LSTM
based models verifying its effectiveness for abnormality detection and
generalization tasks on CUHK Avenue and UCSD datasets.
