  Abnormal activity detection is one of the most challenging tasks in the field
of computer vision. This study is motivated by the recent state-of-art work of
abnormal activity detection, which utilizes both abnormal and normal videos in
learning abnormalities with the help of multiple instance learning by providing
the data with video-level information. In the absence of temporal-annotations,
such a model is prone to give a false alarm while detecting the abnormalities.
For this reason, in this paper, we focus on the task of minimizing the false
alarm rate while performing an abnormal activity detection task. The mitigation
of these false alarms and recent advancement of 3D deep neural network in video
action recognition task collectively give us motivation to exploit the 3D
ResNet in our proposed method, which helps to extract spatial-temporal features
from the videos. Afterwards, using these features and deep multiple instance
learning along with the proposed ranking loss, our model learns to predict the
abnormality score at the video segment level. Therefore, our proposed method 3D
deep Multiple Instance Learning with ResNet (MILR) along with the new proposed
ranking loss function achieves the best performance on the UCF-Crime benchmark
dataset, as compared to other state-of-art methods. The effectiveness of our
proposed method is demonstrated on the UCF-Crime dataset.
