Hermitian scattering behavior for the non-Hermitian scattering center

We study the scattering problem for the non-Hermitian scattering center, which consists of two Hermitian clusters with anti-Hermitian couplings between them. Counterintuitively, it is shown that it acts as a Hermitian scattering center, satisfying $|r| ^{2}+|t| ^{2}=1$, i.e., the Dirac probability current is conserved, when one of two clusters is embedded in the waveguides. This conclusion can be applied to an arbitrary parity-symmetric real Hermitian graph with additional PT-symmetric potentials, which is more feasible in experiment. Exactly solvable model is presented to illustrate the theory. Bethe ansatz solution indicates that the transmission spectrum of such a cluster displays peculiar feature arising from the non-Hermiticity of the scattering center.Comment: 6 pages, 2 figure

Analysis-by-synthesis: Pedestrian tracking with crowd simulation models in a multi-camera video network

For tracking systems consisting of multiple cameras with overlapping field-of-views, homography-based approaches are widely adopted to significantly reduce occlusions among pedestrians by sharing information among multiple views. However, in these approaches, the usage of information under real-world coordinates is only at a preliminary level. Therefore, in this paper, a multi-camera tracking system with integrated crowd simulation is proposed in order to explore the possibility to make homography information more helpful. Two crowd simulators with different simulation strategies are used to investigate the influence of the simulation strategy on the final tracking performance. The performance is evaluated by multiple object tracking precision and accuracy (MOTP and MOTA) metrics, for all the camera views and the results obtained under real-world coordinates. The experimental results demonstrate that crowd simulators boost the tracking performance significantly, especially for crowded scenes with higher density. In addition, a more realistic simulation strategy helps to further improve the overall tracking result