Graph Convolutional Tracking

Junyu Gao      Tianzhu Zhang      Changsheng Xu

 

Figure 1. The pipeline of our GCT, which can jointly perform spatial-temporal target appearance modeling and context-guided feature adaption in a siamese framework. Specifically, we use a ST-GCN to model the historical exemplars with a spatial-temporal graph. Then, the generated ST-feature is combined with the current context feature to learn an adaptive graph, which is used by CT-GCN to produce the adaptive feature. This feature is evaluated on the search image embedding via a cross-correlation layer (XCorr) for target localization.

Abstract


Tracking by siamese networks has achieved favorable performance in recent years. However, most of existing siamese methods do not take full advantage of spatial-temporal target appearance modeling under different contextual situations. In fact, the spatial-temporal information can provide diverse features to enhance the target representation, and the context information is important for online adaption of target localization. To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT) method for high-performance visual tracking. Specifically, the GCT jointly incorporates two types of Graph Concolutional Networks (GCNs) into a siamese framework for target appearance modeling. Here, we adopt a spatial-temporal GCN to model the structured representation of historical target exemplars. Furthermore, a context GCN is designed to utilize the context of the current frame to learn adaptive features for target localization. Extensive results on $4$ challenging benchmarks show that our GCT method performs favorably against state-of-the-art trackers while running around $50$ frames per second.

Related Publications


"Graph Convolutional Tracking"


Junyu Gao, Tianzhu Zhang, Changsheng Xu.
CVPR 2019 (Oral)
[Paper] [Code (Coming soon)] [GCT-OTB-100-Results] [GCT-UAV-123-Results] [GCT-VOT-Results]

Citing


@inproceedings{gao2019gct_cvpr,
  Author    = {Gao, Junyu and Zhang, Tianzhu and Xu, Changsheng},
  Title     = {Graph Convolutional Tracking},
  booktitle = {CVPR},
  Year      = {2019}
}

Experimental Results



Figure 2. Precision and success plots over all the 50 sequences using one-pass evaluation on the OTB-2013 Dataset. The legend contains the area-under-the-curve score and the average distance precision score at 20 pixels for each tracker. Our GCT method performs favorably against the state-of-the-art trackers.

Figure 3. Precision and success plots over all 100 sequences using one-pass evaluation on the OTB-2015 dataset. The legend contains the area-under-the-curve score and the average distance precision score at 20 pixels for each tracker. Our GCT method performs favorably against the state-of-the-art trackers.

Figure 4. Precision and success plots over the 123 sequences using one-pass evaluation on the UAV dataset. The legend contains the area-under-the-curve score and the average distance precision score at 20 pixels for each tracker. Our GCT method performs favorably against the state-of-the-art trackers.

Figure 4. Comparison of EAO scores on VOT2017 challenge and real-time experiment. Our GCT method performs favorably against the state-of-the-art trackers.

 



Thank you for visiting my project!