With respect to multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights take the form of real numbers, attribute values take the form of fuzzy number intuitionistic fuzzy numbers, a new group decision making analysis method is developed. Firstly, some operational laws of fuzzy number intuitionistic fuzzy numbers, score function and accuracy function of fuzzy number intuitionistic fuzzy numbers are introduced. Then a new aggregation operator called induced fuzzy number intuitionistic fuzzy ordered weighted geometric (I-FIFOWG) operator is proposed, and some desirable properties of the I-FIFOWG operators are studied, such as commutativity, idempotency and monotonicity. An I-FIFOWG and FIFWG (fuzzy number intuitionistic fuzzy weighted geometric) operators-based approach is developed to solve the MAGDM under the fuzzy number intuitionistic fuzzy environment. Furthermore, we propose the induced fuzzy number intuitionistic fuzzy ordered weighted averaging (I-FIFOWA) operator. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness. Finally, an illustrative example is given to verify the developed approach.Keywords: Fuzzy number intuitionistic fuzzy numbers; operational laws; fuzzy number intuitionistic fuzzy weighted geometric (FIFWG) operator; induced fuzzy number intuitionistic fuzzy ordered weighted geometric (I-FIFOWG) operator

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The detection of moving objects are important research area for video surveillance and other video processing applications. In this paper, we propose an adaptive approach modeling background and segmenting moving object with non-parametric kernel density estimation. Unlike previous approaches to object detection which detect objects by global threshold, we use a local threshold to reflect temporal persistence. With combined of global threshold and local thresholds, the proposed approach can handle scenes containing gradual illumination variations and noise and has no bootstrapping limitations. Experimental results on different types of videos demonstrate the utility and performance of the proposed approach.