SiamLST: Learning Spatial and Channel-wise Transform for Visual Tracking

Siamese network based trackers regard visual tracking as a similarity matching task between the target template and search region patches, and achieve a good balance between accuracy and speed in recent years. However, existing trackers do not effectively exploit the spatial and inter-channel cues, which lead to the redundancy of pre-trained model parameters. In this paper, we design a novel visual tracker based on a Learnable Spatial and Channel-wise Transform in Siamese network (SiamLST). The SiamLST tracker includes a powerful feature extraction backbone and an efficient cross-correlation method. The proposed algorithm takes full advantages of CNN and the learnable sparse transform module to represent the template and search patches, which effectively exploit the spatial and channel-wise correlations to deal with complicated scenarios, such as motion blur, in-plane rotation and partial occlusion. Experimental results conducted on multiple tracking benchmarks including OTB2015, VOT2016, GOT-10k and VOT2018 demonstrate that the proposed SiamLST has excellent tracking performances

Deformable Siamese Attention Networks for Visual Object Tracking

Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the features of the target template and search image are computed independently in a Siamese architecture. In this paper, we propose Deformable Siamese Attention Networks, referred to as SiamAttn, by introducing a new Siamese attention mechanism that computes deformable self-attention and cross-attention. The self attention learns strong context information via spatial attention, and selectively emphasizes interdependent channel-wise features with channel attention. The cross-attention is capable of aggregating rich contextual inter-dependencies between the target template and the search image, providing an implicit manner to adaptively update the target template. In addition, we design a region refinement module that computes depth-wise cross correlations between the attentional features for more accurate tracking. We conduct experiments on six benchmarks, where our method achieves new state of-the-art results, outperforming the strong baseline, SiamRPN++ [24], by 0.464->0.537 and 0.415->0.470 EAO on VOT 2016 and 2018. Our code is available at: https://github.com/msight-tech/research-siamattn.Comment: CVPR 2020, with code available at: https://github.com/msight-tech/research-siamatt

Iterative Multiple Bounding-Box Refinements for Visual Tracking

Single-object visual tracking aims at locating a target in each video frame by predicting the bounding box of the object. Recent approaches have adopted iterative procedures to gradually refine the bounding box and locate the target in the image. In such approaches, the deep model takes as input the image patch corresponding to the currently estimated target bounding box, and provides as output the probability associated with each of the possible bounding box refinements, generally defined as a discrete set of linear transformations of the bounding box center and size. At each iteration, only one transformation is applied, and supervised training of the model may introduce an inherent ambiguity by giving importance priority to some transformations over the others. This paper proposes a novel formulation of the problem of selecting the bounding box refinement. It introduces the concept of non-conflicting transformations and allows applying multiple refinements to the target bounding box at each iteration without introducing ambiguities during learning of the model parameters. Empirical results demonstrate that the proposed approach improves the iterative single refinement in terms of accuracy and precision of the tracking results