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Joint Template Matching Algorithm for Associated Multi-object Detection
( Jianbin Xie ),( Tong Liu ),( Zhangyong Chen ),( Zhaowen Zhuang ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.1
A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.
A Fast and Robust Algorithm for Fighting Behavior Detection Based on Motion Vectors
( Jianbin Xie ),( Tong Liu ),( Wei Yan ),( Peiqin Li ),( Zhaowen Zhuang ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.11
In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.