Aparna Gullapelly, Barnali Gupta Banik^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1127-1139, 2023, DOI:10.32604/csse.2023.032904

Abstract The field of object tracking has recently made significant progress. Particularly, the performance results in both deep learning and correlation filters, based trackers achieved effective tracking performance. Moreover, there are still some difficulties with object tracking for example illumination and deformation (DEF). The precision and accuracy of tracking algorithms suffer from the effects of such occurrences. For this situation, finding a solution is important. This research proposes a new tracking algorithm to handle this problem. The features are extracted by using Modified LeNet-5, and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method (RCCF). In… More >

Jianming Zhang^1,2,*, Kai Wang^1,2, Yaoqi He^1,2, Lidan Kuang^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 909-927, 2022, DOI:10.32604/cmes.2022.020471

Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the features of different layers are enhanced by the coordinate attention block. We then send these features separately into… More >

Abdollah Amirkhani^1,*, Amir Hossein Barshooi¹, Amir Ebrahimi²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 981-997, 2022, DOI:10.32604/cmc.2022.019001

Abstract The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy… More >