# The of the differences between two corresponding pixels.

The Proposed Technique

We
propose technique to optimizing a real-time panic detection. These
optimizations will include the reduction of the computational complexity while
maintaining a high accuracy. Our real-time technique does not require any prior
knowledge about a given video. Moreover, it will be applicable to various crowd
densities (high-, moderate- and low-density).
The proposed technique works in three steps:

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1-
Different between two
consecutive frames:

The proposed technique starts divide
the video into frames. Then, two consecutive frames t and t?1 are compared pixel by
pixel, to found the absolute values of the differences between two
corresponding pixels. The absolute differential image is defined as follows:

Id (t, t+1) = |It+1 – It|
(1)

It is supposed to
be the value of the tth frame in image sequences. It+1 is
the value of the (t+1)th frame in image sequences. The goal of this
step to reduce the computation complexity and avoid the motion estimation. To achieve
that the result is the positive number that represents the dissimilarity between
pixels, if there is different that mean there is motion.

2-
Calculate the Wavelet Transforms

The result from the
previous step is a positive number used as input of Wavelet Transforms 1.

A brief description of Wavelet Transforms in the following section.
Images as smooth regions interrupted by edges, these edges are often the most interesting
parts of the data both perceptually and in terms of the information they
provide. So, wavelet transform is a powerful tool for data analysis and present
the edges.  A wavelet from the name is
small waves with limited duration. The wavelets transform is set of mathematical
function used to decompose data into different component. The image will divide
into four different subbands as LL (Low frequency), HH (high frequency
diagonal), HL (low frequency horizontal) and LH (low frequency Vertical). This
breaking process can be repeated to have multi-level wavelet components like 2Level,
3Level. In our proposed technique we applied the three level 1 ,2 and 3 of wavelet
transform. Coefficients matrices cH, cV, and cD
(horizontal, vertical, and diagonal, respectively), obtained by wavelet
decomposition of the input which is different between two consecutive frames.  In principle after comparative study between
the results, we found the level 3 is better than other levels because it gave
more accurate result with decrease the false alarm. The goal of this step to found
high frequency part which mean the possibility of panic event is increase.

3-
Sum of all coefficient

After calculate the Wavelet Transforms
in the different between two consecutive frames. We divide each subband into one
block and four blocks to sum the coefficient cH, cD and cV on each block for
all frames. The result of this step we will have the sum of coefficient cH1 , cH2
and cH3 for all frames in case one block and four blocks. The same process
repeated for the cV and cD coefficients. When the value of the sum the coefficient
is high compare to the all previous frames that’s mean there is panic event.  The following equation is used for sum of coefficients:

S =
(2)

Where S denote to sum of coefficient,
n number of frames, and c is the coefficient. The goal of this step to detect
the panic event.

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