Abstract:
An automated surveillance environment is constituted using personal re-identification as
an integral part. Usually, this process is carried out using vision sensors by checking for
the physical appearance of the person that is their color etc. thus putting constraints
in the process of re-identification. This visual based identification comprises a lot of
constraints suggesting the researchers to go for further research finding other ways and
they get to the point of using human gait as a replacement which is much more efficient
and reliable than using visual based analysis in which results may get affected by the
surroundings. Already research has been done in this field, in this research extended
work and is now proposed by using LSTM model over three sensor modalities. Exper iments are conducted over multiple recurrent layers, activation functions and dropout
values for the step data. From these experiments we analyzed that using the less re current layers for the step data model gives almost the same accuracy as the previous
study but, in less time, which reduces the time complexity of the model thus results in
an efficient model.
