暗通道和测度学习的雾天行人再识别

摘要

Person re-identification, identifying the same person in database from non-overlapping camera views, is a challenging task. To reduce the influence of foggy weather on person re-identification, dark channel prior is used to remove haze from input image first. Then, local maximal occurrence representation and metric learning is used to identify the same person’s images which remove haze. Experimental results show that the recognition rate of haze removed achieving 41.75% rank-1 and 81.26% rank-10, is higher than the recognition rate without haze removing which achieve 35.64 % rank-1and 46.75% rank-10.%行人再识别就是给定一张图片，在非重叠的视场行人数据库中，识别出相同的行人。行人再识别面临各种困难，针对来自于雾霾恶劣天气的影响，先利用暗通道先验知识的方法对图像去雾，再用局部最大特征和测度学习的算法对去雾图片行人再识别。实验结果表明，去雾后的行人再识别第1识别率(排名第1的搜寻结果)为41.75%和第10识别率(排名第10的搜寻结果)81.26%相对于有雾条件第1识别率35.64%和第10识别率46.75%。