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Weidai Xia

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Proceedings Article | 11 March 2022 Paper

CMINet: an improved RGBT tracking via cross-modality interaction

Weidai Xia, Dongming Zhou, Yanyu Liu, Yujie Xue

Proceedings Volume 12160, 1216017 (2022) https://doi.org/10.1117/12.2627817

KEYWORDS: Detection and tracking algorithms, RGB color model, Feature extraction, Thermography, Optical tracking, Video, Visible radiation, Convolution, Information fusion

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Visible light and thermal infrared (RGBT) data contain different levels of information about the target, and how to use them effectively plays a crucial role in the representation of the target appearance in RGBT tracking. Existing work has focused on the integration of information from modality-shared features and modality-specific features. These approaches effectively deploy modality-shared cues and modality-specific attributes, ignoring the potential value of multi-layer shared cues of different modalities. To this end, a new multi-feature extraction-based infrared and visible target tracking algorithm is proposed. The tracking algorithm consists of a multi-layer shared fusion network, modal complementary network and target regression network that performs multi-layer modality-sharing, modality-specific and target probability prediction feature learning. Extensive experiments are conducted on the RGBT tracking benchmark dataset to achieve real-time tracking in terms of tracking speed and also show superior performance in comparison with other advanced RGB and RGBT tracking algorithms.

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