Multi-target model visual tracking method based on cost-sensitive three-way decision
A multi-objective model, cost-sensitive technology, applied in the field of surveillance video intelligent analysis, can solve the problems of lack of multiple features, multiple decision types, poor accuracy and robustness, etc., to improve accuracy, good adaptability and stability. The effect of robustness
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[0048] In order to make the object, technical scheme and advantages of the present invention clearer, below in conjunction with embodiment, specifically as figure 1 The shown algorithm flow chart further describes the present invention in detail. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.
[0049] Step 1: Establish multiple target models with different update strategies. The specific description is as follows: The target model should have the following characteristics: keep the characteristics of the tracked target unchanged, adapt to the appearance changes of the target in time, and be able to predict the future appearance changes of the target. Therefore, the present invention firstly saves the target appearance of the initial frame as a constant target feature; secondly, the target appearance updated frame by frame is used as a target feature that can adapt to c...
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[0067] figure 1 It is an algorithm implementation flow chart of the present invention, and the specific implementation is as follows:
[0068] 1. Establish multiple target models with different update strategies to form a set
[0069] 2. Use similarity learning model tracking to obtain multi-model candidate frames s ij , used in the calculations of subsequent Articles 3 and 6;
[0070] 3. Calculate the number N of overlapping pairs of all candidate frames t ;
[0071] 4. Set the cost dictionary for each decision type, according to N t Calculate decision cost;
[0072] 5. Based on the cost-sensitive three-way decision-making theory, calculate the upper and lower boundaries α and β of the three-way decision-making according to the decision cost;
[0073] 6. According to the upper and lower boundaries α and β, use the convolutional neural network discriminator to classify the candidate frames obtained in the second step;
[0074] 7. Use the candidate frame with the highe...
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