  Both recognition and 3D tracking of frontal dynamic objects are crucial
problems in an autonomous vehicle, while depth estimation as an essential issue
becomes a challenging problem using a monocular camera. Since both camera and
objects are moving, the issue can be formed as a structure from motion (SFM)
problem. In this paper, to elicit features from an image, the YOLOv3 approach
is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and
longitudinal distances, a nonlinear SFM model is considered alongside a
state-dependent Riccati equation (SDRE) filter and a newly developed
observation model. Additionally, a switching method in the form of switching
estimation error covariance is proposed to enhance the robust performance of
the SDRE filter. The stability analysis of the presented filter is conducted on
a class of discrete nonlinear systems. Furthermore, the ultimate bound of
estimation error caused by model uncertainties is analytically obtained to
investigate the switching significance. Simulations are reported to validate
the performance of the switched SDRE filter. Finally, real-time experiments are
performed through a multi-thread framework implemented on a Jetson TX2 board,
while radar data is used for the evaluation.
