Improved visual background extractor with adaptive range change
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The visual background extractor (ViBe) has become one of the best motion object detection algorithms because of its good detection results and low memory requirements. However, the ViBe model cannot self-adjust the value range of the parameter that controls the number of samples chosen from the background template. In this paper, two models are proposed to help automatically change the parameter range in different environments. The blink energy model can detect dynamic backgrounds by increasing the range, while the object probability model can prevent corrosion of motion objects by decreasing the range. The experimental results show that our proposed method can both accurately recognize dynamic backgrounds and efficiently prevent object corrosion. In addition, our method shows better performance on benchmark datasets than several commonly used detection algorithms.
KeywordsObject detection ViBe Self-adjust Blink energy Object probability
This work was supported in part by the Key Project of the National Natural Science Foundation of China (No. 61134009), the National Natural Science Foundation of China (Nos. 61473077, 61473078, 61503075, 61603090), Cooperative research funds of the National Natural Science Funds Overseas and Hong Kong and Macao scholars (No. 61428302), National Key Research and Development Plan from Ministry of Science and Technology (2016YFB0302700), Program for Changjiang Scholars from the Ministry of Education, International Collaborative Project of the Shanghai Committee of Science and Technology (No. 16510711100), Innovation Program of Shanghai Municipal Education Commission (No. 14ZZ067), Shanghai Science and Technology Promotion Project form Shanghai Municipal Agriculture Commission (No. 2016-1-5-12), Shanghai Pujiang Program (No. 15PJ1400100), and the Fundamental Research Funds for the Central Universities (No. 2232015D3-32).
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