It is a key problem to get the high resolution and large field of view of dynamic measurement in the ladar technology. In this paper, Charge Coupled Device was synthesized with seams to be a display unit. A reconstruction method is proposed which combines the principles of synthetic aperture and phase retrieval to detect the particle target. Then the non-integer pixel stitching error in a synthetic-aperture ladar with seams is corrected by a method based on the shift theorem of the Fourier transform. In the X experiment, the correlation coefficient of surface l is 0.73217 between the reconstructed image and the simulated particles target image. The pictures from the experiment depicts the particle is not only clearly visible at the normal region but also at the seams. The target information can be recovered well of the high resolution and large field of view with this method.
It is a key problem to get the high resolution and large field of view (FOV) of dynamic measurement in the display technique. In this paper, Charge Coupled Device (CCD) was synthesized with seams to be a display unit. A reconstruction method is proposed which combines the principles of synthetic aperture and phase retrieval to detect the 3D particle target with synthetic CCD. In the simulation experiment, the correlation coefficient of surface l is 0.73217 between the reconstructed image and the simulated particles target image. The pictures from the experiment depicts the particle is not only clearly visible at the normal region but also at the seams. The optical information can be recovered well of the high resolution and large field of view (FOV) with this method.
With the shortcomings of traditional algorithm in video surveillance on low accuracy, poor robustness and unable achieved real-time tracking for multi-targets, this paper presents a Multi-target tracking algorithm, DeepSort, on the base of deep neural network to achieve the end-to-end surveillance video multi-personal target real-time detection and tracking. The high accuracy of target detection by YOLO algorithm provides DeepSort with weaker dependence on detection results, lower interference of occlusion and illumination and improved tracking robustness. Moreover, due to the high redundancy of the surveillance video itself, the difference filter is used to screen the video frames with no foreground targets and small changes, so as to reduce the detection cost and improve the detection and tracking speed. The experimental evaluation of the video surveillance dataset NPLR, the average MOTA of this algorithm is 68.7, the highest value is 86.8; the average speed is 81.6Hz, the highest value is 140Hz. It shows that the end-to-end algorithm is feasible and effective.
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