Geodesy

Video tracking

Benchmark (surveying)

Linguistics

Philosophy

Discriminative model

Tracking (education)

Modality (human–computer interaction)

Pedagogy

Computer science

Video processing

Psychology

Pattern recognition (psychology)

Geography

Image (mathematics)

Feature (linguistics)

Modalities

Sociology

Artificial intelligence

Optical flow

Mutual information

Noise (video)

Computer vision

Social science

IEEE Transactions on Neural Networks and Learning Systems

Low-quality modalities contain not only a lot of noisy information but also some discriminative features in RGB-Thermal (RGBT) tracking. However, the potentials of low-quality modalities are not well explored in existing RGBT tracking algorithms. In this work, we propose a novel duality-gated mutual condition network to fully exploit the discriminative information of all modalities while suppressing the effects of data noise. In specific, we design a mutual condition module, which takes the discriminative information of a modality as the condition to guide feature learning of target appearance in another modality. Such a module can effectively enhance target representations of all modalities even in the presence of low-quality modalities. To improve the quality of conditions and further reduce data noise, we propose a duality-gated mechanism and integrate it into the mutual condition module. To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow. It does not increase much computational cost since we perform optical flow calculation only when the model prediction is unreliable and then execute resampling when the sudden camera motion is detected. Extensive experiments on four RGBT tracking benchmark datasets show that our method performs favorably against the state-of-the-art tracking algorithms.

Duality-Gated Mutual Condition Network for RGBT Tracking