  This paper addresses the problem of joint detection and recounting of
abnormal events in videos. Recounting of abnormal events, i.e., explaining why
they are judged to be abnormal, is an unexplored but critical task in video
surveillance, because it helps human observers quickly judge if they are false
alarms or not. To describe the events in the human-understandable form for
event recounting, learning generic knowledge about visual concepts (e.g.,
object and action) is crucial. Although convolutional neural networks (CNNs)
have achieved promising results in learning such concepts, it remains an open
question as to how to effectively use CNNs for abnormal event detection, mainly
due to the environment-dependent nature of the anomaly detection. In this
paper, we tackle this problem by integrating a generic CNN model and
environment-dependent anomaly detectors. Our approach first learns CNN with
multiple visual tasks to exploit semantic information that is useful for
detecting and recounting abnormal events. By appropriately plugging the model
into anomaly detectors, we can detect and recount abnormal events while taking
advantage of the discriminative power of CNNs. Our approach outperforms the
state-of-the-art on Avenue and UCSD Ped2 benchmarks for abnormal event
detection and also produces promising results of abnormal event recounting.
