A Small Moving Object Detection Algorithm Based on Track in Video Surveillance
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Information Engineering University, PLA Strategic Support Force, Zhengzhou 450000, China
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摘要: 針對視頻監(jiān)控中運動小目標難以檢測的問題，該文提出一種基于航跡的檢測算法。首先，為了降低檢測漏警率，提出區(qū)域紋理特征與差值概率融合的自適應(yīng)前景提取方法；其次，為了降低檢測虛警率，設(shè)計航跡關(guān)聯(lián)的概率計算模型以建立疑似目標在視頻幀間的關(guān)聯(lián)，并設(shè)置雙門限以區(qū)分疑似目標中的真實目標與虛假目標。實驗結(jié)果表明，與多種經(jīng)典算法相比，該算法能對定量范圍內(nèi)的運動小目標以更低的漏警率和虛警率實施準確檢測。
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關(guān)鍵詞:
- 運動目標檢測 /
- 小目標檢測 /
- 航跡關(guān)聯(lián)
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.-
Key words:
- Moving object detection /
- Small object detection /
- Track correlation
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表 1 航跡關(guān)聯(lián)規(guī)則
(1) do； (2) for $u = 1，2，·\!·· ,U$； (3) 尋找${\rm{APT}}{_{U \times V}\;}$中第$u$行中的最大值${a_{uv}}$，記錄其列號$v$； (4) if第$v$列的最大值等于${a_{uv}}$； (5) break； (6) end if； (7) end for； (8) 關(guān)聯(lián)$\{ {\text{Z}}_k^v\} $與$\{ \theta _{k - 1}^u\} $，刪除${\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種算法在不同視頻中的MA值比較
視頻 圖像尺寸(pix) 檢測范圍(%) 像素數(shù) MOG2 ViBe+ Faster RCNN 文獻[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種算法在不同視頻中的FA值比較
視頻 圖像尺寸(pix) 檢測范圍(%) 像素數(shù) MOG2 ViBe+ Faster RCNN 文獻[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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