  Real-time track tracking in high energy physics experiments at colliders
running at high luminosity is very challenging for trigger systems. To perform
pattern-recognition and track fitting in online trigger system, the artificial
Retina algorithm has been introduced in the field. Retina can be implemented in
the state of the art FPGA devices. Our developments use Retina in an iterative
way to identify track for barrel-shape tracker embedded in a high magnetic
field and with high track multiplicity. As a benchmark we simulate LHC t-tbar
events, with a pile-up of 200 and a GEANT-4 based simulation of a 6-layers
barrel tracker detector made of silicon modules. With this sample the
performance of the hardware design (resource usage, latency) is evaluated. Both
efficiency and purity of the Retina fitting are over 90%. Moreover we have also
added a Kalman filter after the Retina fit to improve the resolution on the
track parameters. Our simulation results show that the Kalman filter can work
well together with the Retina algorithm to find track through t-tbar event and
provides high resolutions of the reconstructed parameters.
