A hardware and software design for an image acquisition module

Wan, Jia; Zhou, Jian; Tang, Ling Yi; Tong, Mei Song

doi:10.1109/piers-fall.2017.8293300

Cited by 2 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is assumed that the actual integration time of image lag corresponding to the background light is N db and that of the target light is N de . According to Equations (4) and (8), the Equation (9) is given by: (9) Assuming that the moving target light is brighter than the background light. Because I e is greater than I b , it can be seen from Equations (8) and (9) that when N de is smaller, N d is larger.…”

Section: Analysis Of the Target Tracking Algorithm For High-speed Movmentioning

confidence: 99%

“…According to Equations (4) and (8), the Equation (9) is given by: (9) Assuming that the moving target light is brighter than the background light. Because I e is greater than I b , it can be seen from Equations (8) and (9) that when N de is smaller, N d is larger. Assuming the target is moving at a speed of V 0 pixels per frame, and N 0 equals to 1/V 0 .…”

Section: Analysis Of the Target Tracking Algorithm For High-speed Movmentioning

confidence: 99%

“…Target tracking algorithm based on personal computer (PC) needs to transfer data to PC and then process data by central processing unit (CPU) or graphics processing unit (GPU) [8]. Although the data acquisition rate via universal serial bus (USB) 3.0 can reach 320 M Byte/s [9], data transmitted directly to PC via USB3.0 will also cause some time delay. It will weaken the real‐time performance of target tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High‐speed target tracking algorithm for the pulse‐sequence‐based image sensor

Wang

Gao

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

This paper proposed a high-speed target tracking algorithm based on the pulse-sequencebased image sensor. The algorithm separates the target from the background through the statistical distribution of the pulses in a single frame of data. In order to achieve more accurate target tracking, the phenomenon of image lag in the process of capturing the high-speed moving target is studied. The positioning deviation is compensated by estimating the image lag length of the moving target. The algorithm is implemented with pipeline processing on field-programmable gate array (FPGA). The positioning accuracy of the algorithm is verified with input data from a pulse-sequence image sensor model. The verification results show that the maximum position deviation of the target at different speeds is 0.94 pixels. In the experiment of tracking a 40 m/s moving ball, this algorithm can provide real-time position information within 44 ns.

show abstract