Reducing False Alarms in Video Surveillance by Deep Feature Statistical
  Modeling
Xavier Bou Aitor Artola Thibaud Ehret Gabriele Facciolo Jean-Michel Morel Rafael Grompone von Gioi
Detecting relevant changes is a fundamental problem of video surveillance.
Because of the high variability of data and the difficulty of properly
annotating changes, unsupervised methods dominate the field. Arguably one of
the most critical issues to make them practical is to reduce their false alarm
rate. In this work, we develop a method-agnostic weakly supervised a-contrario
validation process, based on high dimensional statistical modeling of deep
features, to reduce the number of false alarms of any change detection
algorithm. We also raise the insufficiency of the conventionally used
pixel-wise evaluation, as it fails to precisely capture the performance needs
of most real applications. For this reason, we complement pixel-wise metrics
with object-wise metrics and evaluate the impact of our approach at both pixel
and object levels, on six methods and several sequences from different
datasets. Experimental results reveal that the proposed a-contrario validation
is able to largely reduce the number of false alarms at both pixel and object
levels.