In order to solve the object tracking under occlusion,this paper proposed the adaptively tracking algorithm based on color features. Adaptively divided the object using fuzzy K-means clustering algorithm,and weighted with the sub-regions the monotone decreasing kernel function. It updated The object model through the mean value of sub-regions’color,so the calculation was simple. During the object tracking,used the method of integral matching,combining with the adaptive Kalman filter,resolved the object tracking under occlusion effectively. The experiments show that the new algorithm can track the object exactly.

Study on vision object tracking based on adaptive object segmentation

Generally fast algorithms for motion estimation have low precisionBased on the basic theory and rule of block matching,a new fast motion estimation algorithm which uses maximum grads and more cycle conjugate search algorithm to improve searching approach,is presentedIts speed is comparable to typical fast algorithm and its precision is compariable to the exhaustive searching algorithm,meanwhile it has veracity,simple algorithm path and is easy to be realizedThe results of the comparison are also presented

The Effective Improvement of Conjugate Search Algorithm

Recent developments in biometric recognition analysis include various imaginative intelligence concepts for identifying multimodal friction rides based on feature descriptions to solve the complexities in security and person identification applications. For information security, the Research uses multimodal screening analyses from finger veins, finger knuckles, iris, palm prints, and fingerprints. Most traditional methodologies only concentrate on object region regions, which fails to identify the feature relation and entity variation. The problem arises from the multi-correlation feature dimension, which increases the non-relation image registration and structural properties in entity resolution. Due to higher degradation in entity resolution, finding ridges creates false occlusion, causing a higher dimensionality ratio, lower precision rate, recall rate, and more false positives to provide lower identification accuracy. To resolve this problem, we propose an artificial intelligence based on AI-powered Multimodal biometrics recognition using deep scalar feature engineering with Optimal Particle Swarm Intelligence (OPSI) feature selection with Hyper capsule Gated LenetCNN. Initially, the multimodal biometric dataset is pre-processed with an adaptive Gaussian mean filter to normalize the images, and the Iterative pattern Slice Fragment Clustering (IPSFC) is applied for entity markings. Then, a vector Pyramid Scene Parsing Network is used for segmentation. Then, to reduce the feature dimension, the Optimal Particle Swarm Intelligence (OPSI) feature selection is applied to minimize the non-relation features. Then, LSTM- Hyper Capsnet CNN is used to identify the biometric data. Our methodology involves extracting pertinent biometric data features and utilizing deep learning algorithms to enhance identification accuracy. The proposed deep optimal feature engineering approach is introduced, allowing the model to prioritize and select features that significantly increase the identification accuracy while minimizing feature redundancy. The output result is evaluated, and improved accuracy, sensitivity, specificity, and F1 measure performance are achieved, significantly increasing the precision rate based on the proposed CNN classification. 

A New Innovation in Biometric Recognition Using Agentic AI Based on Swarm Feature Engineering with Ensemble LenetCNN

To detect IWST, foreign and domestic scholars put forward many meaningful detection methods. However, most of the
algorithms are too much complex in the calculation to meet real-time and reliability requirements in practical application.
A simple self-adaptive threshold algorithm to capture and track the IWST is presented in this paper. Testing results
showed that algorithm not only effectively extract IWST and track it through the clouds in the sky background, but also
has a strong robustness for the interference of background noises.

Self-adaptive threshold tracking algorithm of infrared weak-small targets

In order to save the computational time of and apply in engineering practice well, a lot of correlation matching algorithms have been proposed at present. A practical effective method based on small diamond search template is presented in this paper. By comparing with the classical algorithm based on the large diamond, it is very pleased with the results. The algorithm presented not only reduced the computational complexity and improved the computational speed, but also ensured the search precision of correlation matching. It is fully able to satisfy the requirement of real-time video tracking, and be pleased greatly with the accuracy and reliability of the real-time video tracking. Keywords: Video tracking, Correlation matching, Real time, Large diamond, Dynamic range, Center-offset, Vector motion, Linear search strategy, Correlation coefficient, Small diamond search template. 1. INTRODUCTION In real-time video tracking, the correlation matching technique has been applied in all aspects. In order to save the computational time and apply in engineering practice well, a lot of algorithms have been proposed at present, such as Various Gray Level Correlation Algorithm, FFT algorithms, Sequence Resemblance Test Algorithm, and Various Resolution Correlation Algorithm, etc. However, most of the methods have low tracking accuracy and reliability, and their computations are still complex and can not meet the actual needs of real-time video [1]tracking . In order to make the correlation matching technique be applie d in the real-time video tracking, a new fast and efficient matching algorithm based on the gradient descent search strate gy is presented in this paper. It takes advantage of the characteristics that the movement of the feature point is around the center of previous field template in the video tracking, according to the center-offset of the motion vectors distribution and the idea of parallel processing, the algorithm designed a small diamond search template based on multi-step search strategy. The algorithm not only reduces the computational complexity and improves the computational speed, but also ensures the search precision of correlation matching. By comparing with the classical algorithm based on the large diamond, it is very pleased with the results.

An effective matching algorithm in real-time video tracking

Biometrics contain rich personal identity information, so biometrics technology has become a critical identification method. Currently, the disadvantages of single-modal biometric recognition technology in application universality and recognition accuracy have become increasingly prominent. In contrast, multi-modal biometric recognition technology integrates several complementary biometric, which expands the scope of biometric identification and improves the accuracy and security. In this paper, we mainly review the previously published viewpoints, summarize and analyze the biometric recognition based on hand veins, and introduce fusion methods at different fusion levels.

A Survey of Multimodal Biometric Recognition Based on Hand Vein

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Self-adaptive threshold tracking algorithm of infrared weak-small targets

An effective matching algorithm in real-time video tracking

A Survey of Multimodal Biometric Recognition Based on Hand Vein