基于脉冲图像传感器的高精度高速目标追踪

Pulse Image Sensor-Based High-Precision and High-Speed Target Tracking

针对脉冲图像传感器,提出一种高精度高稳定性的高速目标追踪算法.首先,介绍脉冲图像传感器的原理;其次,结合传感器脉冲密度特性改进传统视觉背景提取(Vibe)算法,去除传统Vibe算法中存在的鬼影和空洞问题,提高运动检测的完整性;然后,结合运动检测,对传统均值漂移(MS)追踪算法进行改进,提高目标追踪的精度和稳定性;最后,通过图像重构完成场景再现与目标追踪.在3个高速场景的实验中:与直接应用于图像序列的传统MS算法相比,所提算法对高速目标的最大追踪误差分别从11.0454降低至2.2361,从14.1421降低至5.0000,从26.1725降低至5.0990;目标追踪的位置标准差从7.9879降低至2.0393,从12.0790降低至2.7454,从14.4591降低至3.5654.综上所述,所提算法能够有效提高目标的追踪精度和追踪稳定性,能更好地适用于脉冲图像传感器.

A highspeed target tracking algorithm with high accuracy and stability is proposed for pulse image sensors.First,the principle of a pulse image sensor is introduced.Second,the traditional visual background extraction(Vibe)algorithm is improved by combining the pulse density characteristics of the sensor to remove the ghost and hole issues in the traditional Vibe algorithm,this further improves the integrity of motion detection.Subsequently,combined with motion detection,the traditional mean shift(MS)tracking algorithm is enhanced to improve the accuracy and stability of target tracking.Finally,scene reconstruction and target tracking are completed via image reconstruction.In the three highspeed scenes experiments,compared with the traditional MS algorithm,which is directly applied to image sequences,the maximum tracking error of the proposed algorithm for highspeed targets reduced from 11.0454 to 2.2361,from 14.1421 to 5.0000,and from 26.1725 to 5.0990,respectively.The position standard deviation of target tracking decreased from 7.9879 to 2.0393,from 12.0790 to 2.7454,and from 14.4591 to 3.5654,respectively.In summary,the proposed algorithm can effectively improve target tracking accuracy and stability and is more suitable for pulse image sensors than the other algorithms.