Abstract: In this paper, we present a video based smoke detection
algorithm based on TVL1 optical flow estimation. The main part
of the algorithm is an accumulating system for motion angles and
upward motion speed of the flow field. We optimized the usage of
TVL1 flow estimation for the detection of smoke with very low smoke
density. Therefore, we use adapted flow parameters and estimate the
flow field on difference images. We show in theory and in evaluation
that this improves the performance of smoke detection significantly.
We evaluate the smoke algorithm using videos with different smoke
densities and different backgrounds. We show that smoke detection
is very reliable in varying scenarios. Further we verify that our
algorithm is very robust towards crowded scenes disturbance videos.
Abstract: Intelligent Video-Surveillance (IVS) systems are
being more and more popular in security applications. The analysis
and recognition of abnormal behaviours in a video sequence has
gradually drawn the attention in the field of IVS, since it allows
filtering out a large number of useless information, which guarantees
the high efficiency in the security protection, and save a lot of human
and material resources. We present in this paper ADABeV, an
intelligent video-surveillance framework for event recognition in
crowded scene to detect the abnormal human behaviour. This
framework is attended to be able to achieve real-time alarming,
reducing the lags in traditional monitoring systems. This architecture
proposal addresses four main challenges: behaviour understanding in
crowded scenes, hard lighting conditions, multiple input kinds of
sensors and contextual-based adaptability to recognize the active
context of the scene.