Correlation Filter Tracker With Sample-Reliability Awareness and Self-Guided Update

Zhou, Lifang; Li, Jiaqi; Lei, Bangjun; Li, Weisheng; Leng, Jiaxu

doi:10.1109/tcsvt.2022.3199325

Cited by 6 publications

(1 citation statement)

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“…Furthermore, visual object tracking performance is improved by establishing feature extraction. Feature extraction is the most essential factor for object tracking [13]. When compared to the features of a deep Convolutional Neural Network (DCNN) with descriptors of the conventional images, the features are more effective and intuitive.…”

Section: Introductionmentioning

confidence: 99%

Visual Object Tracking Based on Modified LeNet-5 and RCCF

Gullapelly¹,

Banik²

2023

Computer Systems Science and Engineering

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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 Modified LeNet-5, the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers. The high-level, middle-level, and handcraft features are extracted from the modified LeNet-5 network. The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object. The LeNet features are more suitable for a target appearance change in object tracking. Extensive experiments were conducted by the Object Tracking Benchmarking (OTB) databases like OTB50 and OTB100. The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 93.8% and 92.5%. The average running frame rate reaches 42 frames per second, which can meet the real-time requirements.

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Section: Introductionmentioning

confidence: 99%