Abstract:
Anomaly detection in video data has been a challenge always. After the introduction
of many state-of-art designs, this still poses a challenge as those systems may
fail to work in all types of environments. Even though many supervised methods
claimed to have some good results in this domain, supervised systems may not
be suitable for all the contexts such as in an open area, any type of anomaly can
occur and it can be very di cult to train a system in a supervised manner to identify
an unanticipated anomaly. On the other hand, it would be di cult for the
user to annotate data each time when they change the context under surveillance
for the device. Thus the ultimate solution should be an unsupervised solution
with a appreciable accuracy. Recently deep learning techniques have emerged in
many areas of computer science based solutions and so it is involved for anomaly
detection tasks also. In this research, deep learning techniques are involved to
solve the problem of video stream based anomaly detection of crowds.
