  Anomaly activities such as robbery, explosion, accidents, etc. need immediate
actions for preventing loss of human life and property in real world
surveillance systems. Although the recent automation in surveillance systems
are capable of detecting the anomalies, but they still need human efforts for
categorizing the anomalies and taking necessary preventive actions. This is due
to the lack of methodology performing both anomaly detection and classification
for real world scenarios. Thinking of a fully automatized surveillance system,
which is capable of both detecting and classifying the anomalies that need
immediate actions, a joint anomaly detection and classification method is a
pressing need. The task of joint detection and classification of anomalies
becomes challenging due to the unavailability of dense annotated videos
pertaining to anomalous classes, which is a crucial factor for training modern
deep architecture. Furthermore, doing it through manual human effort seems
impossible. Thus, we propose a method that jointly handles the anomaly
detection and classification in a single framework by adopting a
weakly-supervised learning paradigm. In weakly-supervised learning instead of
dense temporal annotations, only video-level labels are sufficient for
learning. The proposed model is validated on a large-scale publicly available
UCF-Crime dataset, achieving state-of-the-art results.
