CMINet: an improved RGBT tracking via cross-modality interaction

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.