  Accurate measuring the location and orientation of individual particles in a
beam monitoring system is of particular interest to researchers in multiple
disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel
sensors have the great potential to realize long-term stable measurement with
considerable precision. In this paper, we introduce deep learning to analyze
patterns in the beam projection image to facilitate three-dimensional
reconstruction of particle tracks. We propose an end-to-end neural network
based on segmentation and fitting for feature extraction and regression. Two
segmentation branches, named binary segmentation and semantic segmentation,
perform initial track determination and pixel-track association. Then pixels
are assigned to multiple tracks, and a weighted least squares fitting is
implemented with full back-propagation. Besides, we introduce a center-angle
measure to judge the precision of location and orientation by combining two
separate factors. The initial position resolution achieves 8.8 $\mu m$ for the
single track and 11.4 $\mu m$ (15.2 $\mu m$) for the 1-3 tracks (1-5 tracks),
and the angle resolution achieves 0.15$^{\circ}$ and 0.21$^{\circ}$
(0.29$^{\circ}$) respectively. These results show a significant improvement in
accuracy and multi-track compatibility compared to traditional methods.
