  Anomaly detection in crowd videos has become a popular area of research for
the computer vision community. Several existing methods generally perform a
prior training about the scene with or without the use of labeled data.
However, it is difficult to always guarantee the availability of prior data,
especially, for scenarios like remote area surveillance. To address such
challenge, we propose an adaptive training-less system capable of detecting
anomaly on-the-fly while dynamically estimating and adjusting response based on
certain parameters. This makes our system both training-less and adaptive in
nature. Our pipeline consists of three main components, namely, adaptive 3D-DCT
model for multi-object detection-based association, local motion structure
description through saliency modulated optic flow, and anomaly detection based
on earth movers distance (EMD). The proposed model, despite being
training-free, is found to achieve comparable performance with several
state-of-the-art methods on the publicly available UCSD, UMN, CHUK-Avenue and
ShanghaiTech datasets.
