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In order to solve the most difficult problem of the architectural model established by CNN in solving specific problems, which results in parameter overflow and inefficient training, an optimization algorithm for nonlinear convolutional neural networks based on improved chameleon swarm algorithm is proposed. This article mainly introduces the use of Chameleon Swarm Optimization (PSO) algorithm to research the parameters of CNN architecture, solve them, and achieve the optimization of the optimization model.Although the number of parameters that need to be set up in CNN is very large, this method can find better testing space for Alexnet samples with 5 different images. In order to improve the performance of the improved pruning algorithms, two candidate pruning algorithms are also proposed. The experimental results show that compared with the traditional Alexnet model, the improved pruning method improves the image recognition ability of the Caffe primary parameter set from 1.3% to 5.7%. Conclusion: This method has wide applicability and can be applied to most neural networks which do not require any special functional modules of the Alexnet network model. [ABSTRACT FROM AUTHOR]

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Object drift determination is a crucial issue in long-term tracking. Most existing object drift determination criteria require manually selecting different thresholds on different datasets to determine whether the object is lost. In this case, choosing the appropriate threshold is a complex problem. An object drift determination network based on dual-template joint decision-making is proposed to address this issue. The proposed object drift determination network not only does not require selection of thresholds on different datasets, and can be used as a plug-and-play module of short-term visual tracking algorithm to achieve long-term visual tracking tasks with good generalization ability. The proposed object drift determination network is applied to four short-term baseline trackers and constructs four long-term visual tracking algorithms. Experimental results verify that all four improved algorithms significantly improve long-term visual tracking performance compared to the original algorithms. In addition, the determined speed of the object drift determination network proposed in this paper reaches 111 FPS, which has little effect on the long-term visual tracking speed. • New drift determination network without threshold selection is proposed. • New template update criterion is proposed to ensure the quality of templates. • Capable of acting as a plug-and-play module for most basic trackers. • The effectiveness of the proposed method is verified on five public datasets. [ABSTRACT FROM AUTHOR]