Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking

By Chenglong Li, Hui Cheng, Shiyi Hu, Xiaobai Liu, Jin Tang, Liang Lin. IEEE Transactions on Image Processing, 2016

356 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Visible and Infrared Image Fusion Using Deep Learning (2023, methodology): “GTOT [182], RGBT234 [9], and LasHeR [183] are mainly used for RGBT tracking.”
Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “We can see that our method FastICENet(acc) achieves the highest segmentation accuracy (i.e., 91.860 % mIoU), but its speed is slow.”
RFN-Nest: An end-to-end residual fusion network for infrared and visible images (2021, methodology): “Three RGBT datasets are used to train our RFN module, namely GTOT [56], VT821 [57], VT1000 [58].”
LasHeR: A Large-Scale High-Diversity Benchmark for RGBT Tracking (2021, methodology): “[1], [12], [13] propose collaborative sparse representation models for RGBT tracking in Bayesian”
RGB-T Object Tracking: Benchmark and Baseline (2018, methodology): “[2] 2012 MEEM[54]+RGBT 2014 DSST[50] 2014 SAMF[51] 2014 KCF[44]+RGBT 2015 SRDCF[52] 2015 SOWP[11] 2015 SOWP[11]+RGBT 2015 CSR[6] 2016 C-COT[48] 2016 CSR-DCF[53] 2017 CSR-DCF[53]+RGBT 2017 CFnet[39] 2017 CFnet[39]+RGBT 2017 ECO[49] 2017 SGT 2017 IV.”
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images (2023, methodology): “To train the embedded fusion network (LRRNet module) with DFAT, four limited RGBT datasets are utilized: GTOT [45], VT821 [46], VT1000 [47], LasHeR [48].”