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一種基于可變形部件模型的快速對象檢測算法

李春偉,  于洪濤,  李邵梅,  卜佑軍

李春偉, 于洪濤, 李邵梅, 卜佑軍. 一種基于可變形部件模型的快速對象檢測算法[J]. 電子與信息學(xué)報, 2016, 38(11): 2864-2870. doi: 10.11999/JEIT160080
引用本文: 李春偉, 于洪濤, 李邵梅, 卜佑軍. 一種基于可變形部件模型的快速對象檢測算法[J]. 電子與信息學(xué)報, 2016, 38(11): 2864-2870. doi: 10.11999/JEIT160080
LI Chunwei, YU Hongtao, LI Shaomei, BU Youjun. Rapid Object Detection Algorithm Based on Deformable Part Models[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2864-2870. doi: 10.11999/JEIT160080
Citation: LI Chunwei, YU Hongtao, LI Shaomei, BU Youjun. Rapid Object Detection Algorithm Based on Deformable Part Models[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2864-2870. doi: 10.11999/JEIT160080

一種基于可變形部件模型的快速對象檢測算法

doi: 10.11999/JEIT160080
基金項目: 

國家自然科學(xué)基金(61572519, 61521003)

Rapid Object Detection Algorithm Based on Deformable Part Models

Funds: 

The National Natural Science Foundation of China (61572519, 61521003)

  • 摘要: 為了解決可變形部件模型檢測過程中的速度瓶頸問題，該文針對模型的檢測流程，提出一種結(jié)合快速特征金字塔計算的級聯(lián)可變形部件模型。由于模型的檢測速度主要取決于特征計算以及對象定位這兩個過程，提出一種兩階段的加速算法：首先采用尺度上稀疏采樣的特征金字塔來近似表示精細采樣的多尺度圖像特征，以加快特征計算過程；然后在定位過程中結(jié)合級聯(lián)算法，以一個序列模型順序地評估各個部件，從而快速剪除大部分可能性較小的對象假設(shè)，以加快對象定位過程。在PASCAL VOC 2007和INRIA數(shù)據(jù)集上的實驗結(jié)果表明，該算法可以明顯加快檢測速度，而檢測精度僅略有下降。
    Abstract: To solve the speed bottleneck of deformable part models in the detection process, this paper proposes a cascade deformable part model with rapid computation of feature pyramids for the detection process of the model. Because the speed of the detection is mainly determined by the two processes of the feature computation and the object location, a two-stage speedup algorithm is proposed. Firstly, sparsely-sampled feature pyramids on the scale are utilized to approximate finely-sampled multi-scale image features to speed up the process of feature computation. Then combined with the cascade algorithm in the location process, a sequence model is utilized to evaluate individual parts sequentially so as to rapidly prune most object hypotheses of small possibilities in order to speed up the process of object location. The experimental results on PASCAL VOC 2007 dataset and INRIA dataset show that the algorithm in the paper apparently speeds up the speed of detection with minor loss in detection precision.
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出版歷程
  • 收稿日期:  2016-01-19
  • 修回日期:  2016-06-08
  • 刊出日期:  2016-11-19

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