  Anomaly detection in crowds enables early rescue response. A plug-and-play
smart camera for crowd surveillance has numerous constraints different from
typical anomaly detection: the training data cannot be used iteratively; there
are no training labels; and training and classification needs to be performed
simultaneously. We tackle all these constraints with our approach in this
paper. We propose a Core Anomaly-Detection (CAD) neural network which learns
the motion behavior of objects in the scene with an unsupervised method. On
average over standard datasets, CAD with a single epoch of training shows a
percentage increase in Area Under the Curve (AUC) of 4.66% and 4.9% compared to
the best results with convolutional autoencoders and convolutional LSTM-based
methods, respectively. With a single epoch of training, our method improves the
AUC by 8.03% compared to the convolutional LSTM-based approach. We also propose
an Expectation Maximization filter which chooses samples for training the core
anomaly-detection network. The overall framework improves the AUC compared to
future frame prediction-based approach by 24.87% when crowd anomaly detection
is performed on a video stream. We believe our work is the first step towards
using deep learning methods with autonomous plug-and-play smart cameras for
crowd anomaly detection.
