  Suspicious behavior is likely to threaten security, assets, life, or freedom.
This behavior has no particular pattern, which complicates the tasks to detect
it and define it. Even for human observers, it is complex to spot suspicious
behavior in surveillance videos. Some proposals to tackle abnormal and
suspicious behavior-related problems are available in the literature. However,
they usually suffer from high false-positive rates due to different classes
with high visual similarity. The Pre-Crime Behavior method removes information
related to a crime commission to focus on suspicious behavior before the crime
happens. The resulting samples from different types of crime have a high-visual
similarity with normal-behavior samples. To address this problem, we
implemented 3D Convolutional Neural Networks and trained them under different
approaches. Also, we tested different values in the number-of-filter parameter
to optimize computational resources. Finally, the comparison between the
performance using different training approaches shows the best option to
improve the suspicious behavior detection on surveillance videos.
