Multi-modal fusion for end-to-end RGB-T tracking

The paper is published in ICCVW 2019 (VOT2019).

arXiv, ICCV2019 openaccess version, poster.

Citation

Please cite our paper if you are inspired by this idea.

@InProceedings{Zhang_2019_ICCVW,
author = {Zhang, Lichao and Danelljan, Martin and Gonzalez-Garcia, Abel and van de Weijer, Joost and Shahbaz Khan, Fahad},
title = {Multi-Modal Fusion for End-to-End RGB-T Tracking},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2019}
}

Instructions

We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a carefully designed target prediction network trained end-to-end using a discriminative loss. We analyze the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier. We consider several fusion mechanisms acting at different levels of the framework, including pixel-level, feature-level and response-level. Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities. As data to train our model, we generate a large-scale RGB-T dataset by considering an annotated RGB tracking dataset (GOT-10k) and synthesizing paired TIR images using an image-to-image translation approach. We perform extensive experiments on VOT-RGBT2019 dataset and RGBT210 dataset, evaluating each type of modality fusing on each model component. The results show that the proposed fusion mechanisms improve the performance of the single modality counterparts. We obtain our best results when fusing at the feature-level on both the IoU-Net and the model predictor, obtaining an EAO score of 0.391 on VOT-RGBT2019 dataset. With this fusion mechanism we achieve the state-of-the-art performance on RGBT210 dataset.

Pre-trained models

The pre-trained mfDiMP;

The pre-trained DiMP.

Raw results

The results are available for comparison.

[1] Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte. Learning Discriminative Model Prediction for Tracking. In ICCV 2019.