A Small Moving Object Detection Algorithm Based on Track in Video Surveillance
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摘要: 针对视频监控中运动小目标难以检测的问题，该文提出一种基于航迹的检测算法。首先，为了降低检测漏警率，提出区域纹理特征与差值概率融合的自适应前景提取方法；其次，为了降低检测虚警率，设计航迹关联的概率计算模型以建立疑似目标在视频帧间的关联，并设置双门限以区分疑似目标中的真实目标与虚假目标。实验结果表明，与多种经典算法相比，该算法能对定量范围内的运动小目标以更低的漏警率和虚警率实施准确检测。Abstract: To solve the problem that small moving object is difficult to be detected in video surveillance, a track-based detection algorithm is proposed. Firstly, in order to reduce missing alarm, an adaptive foreground extraction method combining regional texture features and difference probability is presented. Then, for reducing false alarm, the probability computing model of track correlation is designed to establish the correlation of suspected objects between frames, and double-threshold are set to distinguish between true and false positive. Experimental results show that compared with many classical algorithms, this algorithm can accurately detect small moving object within the quantitative range with lower missing and false alarm.
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Key words:
- Moving object detection /
- Small object detection /
- Track correlation
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表 1 航迹关联规则
(1) do； (2) for $u = 1，2，·\!·· ,U$； (3) 寻找${\rm{APT}}{_{U \times V}\;}$中第$u$行中的最大值${a_{uv}}$，记录其列号$v$； (4) if第$v$列的最大值等于${a_{uv}}$； (5) break； (6) end if； (7) end for； (8) 关联$\{ {\text{Z}}_k^v\} $与$\{ \theta _{k - 1}^u\} $，删除${\rm{APT}}{_{U \times V}}\;$中的第$u$行和第$v$列元素，
$U = U - 1 ,V = V - 1$；(9) while ${\rm{APT}}{_{U \times V}}\;$中存在元素大于0。 
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表 2 5种算法在不同视频中的MA值比较
视频 图像尺寸(pix) 检测范围(%) 像素数 MOG2 ViBe+ Faster RCNN 文献[5] 本文算法 blizzard $ 720 \times 480$ 0.10～0.12 345～414 0.15 0.78 1.00 1.00 0.28 highway $ 320 \times 240$ 0.12～0.30 92～230 1.00 0.16 1.00 0.50 0.05 Camera 01 $ 1920 \times 1080$ 0.01～0.12 207～2488 0.38 0.29 1.00 0.86 0.11 Camera 02 $ 1920 \times 1080$ 0.01～0.12 207～2488 0.39 0.28 1.00 0.77 0.13
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表 3 5种算法在不同视频中的FA值比较
视频 图像尺寸(pix) 检测范围(%) 像素数 MOG2 ViBe+ Faster RCNN 文献[5] 本文算法 blizzard $ 720 \times 480$ 0.10～0.12 345～414 0.71 0.21 0.00 0.00 0.18 highway $ 320 \times 240$ 0.12～0.30 92～230 0.37 0.61 0.00 0.00 0.29 Camera 01 $ 1920 \times 1080$ 0.01～0.12 207～2488 0.51 0.25 0.00 0.00 0.13 Camera 02 $ 1920 \times 1080$ 0.01～0.12 207～2488 0.52 0.17 0.00 0.00 0.14
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