Moving Object Detection Using Background Subtraction by Soharab Hossain Shaikh, Khalid Saeed, Nabendu Chaki

By Soharab Hossain Shaikh, Khalid Saeed, Nabendu Chaki

This Springer short offers a entire survey of the present methodologies of history subtraction tools. It offers a framework for quantitative functionality assessment of alternative techniques and summarizes the general public databases on hand for study reasons. This famous technique has purposes in relocating item detection from video captured with a stationery digital camera, setting apart foreground and history gadgets and item type and popularity. The authors establish universal demanding situations confronted through researchers together with sluggish or unexpected illumination switch, dynamic backgrounds and shadow and ghost areas. This short concludes with predictions at the destiny scope of the equipment. transparent and concise, this short equips readers to figure out the simplest history subtraction technique for a selected undertaking. it's a resource for execs and researchers operating during this field.

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Iv. g. mean, standard deviation ( sd), minimum ( min) and maximum ( max) of intensity values. Previous b frames in the temporal sequence with respect to the present frame are considered for computing the pixel statistics. The block statistics vector for each block Bij is calculated as follows ( ) BStat Bij = Vmeanij , Vsdij , Vminij , Vmaxij  ∀Bij  FMaskBP vmean ( xi ) = mean ( xi t − b , xi t − b + 1 , xi t −1 ) is the mean of intensity values of the pixel xi in past b number of temporal frames from t-b to t-1.

46 4 Moving Object Detection: A New Approach Fig. 11 Performance evaluation. (a) Precision (b) Specificity Fig. 12 Performance evaluation. (a) FPR and FNR (b) PWC A study is made to check for the accuracy of recognition of the proposed method. 12. 11 two plots are presented. 11 (a) shows the results on the basis of precision measure for a set of 900 frames (samples are taken 10 frames apart). It is found that more than 90 % precision is obtained for most of the frames. 11 (b) the results of the same set of frames are shown for the specificity measure.

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