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- 2017
基于小波域特征和贝叶斯估计的目标检测算法
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Abstract:
摘要： 为了改进目标检测算法,在小波域建立基于贝叶斯概率估计的模型,得到一个自适应最佳阈值,并利用该阈值得到待检测的目标。对待检测的图像序列进行基于滑动窗口的双Haar小波变换,对小波变换后的低频分量建立基于核密度函数的贝叶斯概率估计模型,通过训练和学习,得到自适应的最佳阈值,利用该阈值对低频分量进行判别,得到只含有目标的二值化图像。选取室内室外一个和多个运动目标的6个视频序列对该算法的有效性进行检验,并同其他算法相比,可以给出更好的检测结果。
Abstract: In order to improve the detection algorithm, Bayesian probability estimation model in wavelet domain was built to get a robust threshold, and the detected object could be obtained with the adaptive threshold. Moving Window-Based Double Haar Wavelet Transform for detected image sequence was finished. Bayesian probobility estimation model based on kernel density function was built for low frequency part, and adaptive threshold could be obtained after training and studying. With the threshold to judge the low frequnency part, the binary image could be got. Six video sequences with one targe and multiple targets outdoor and indoor were employed to evaluate the effectiveness of presented algorithm. Experimental results showed that it could give a better detecting results
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