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Siamese Box Adaptive Network for Visual Tracking
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TLDR
SiamBAN as discussed by the authors views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN.Abstract:
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. Unfortunately, they typically call for tedious and heuristic configurations. To address this issue, we propose a simple yet effective visual tracking framework (named Siamese Box Adaptive Network, SiamBAN) by exploiting the expressive power of the fully convolutional network (FCN). SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN. The no-prior box design avoids hyper-parameters associated with the candidate boxes, making SiamBAN more flexible and general. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019, OTB100, NFS, UAV123, and LaSOT demonstrate that SiamBAN achieves state-of-the-art performance and runs at 40 FPS, confirming its effectiveness and efficiency. The code will be available at this https URL.read more
Citations
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Journal ArticleDOI
Deep Learning for Visual Tracking: A Comprehensive Survey
TL;DR: This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics, and extensively evaluates and analyzes the leading visualtracking methods.
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TL;DR: A simple target-aware Siamese graph attention network for general object tracking that establishes part-to-part correspondence between the target and the search region with a complete bipartite graph, and applies the graph attention mechanism to propagate target information from the template feature to the search feature.
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Siamese Network for RGB-D Salient Object Detection and Beyond.
TL;DR: The proposed JL-DCF module provides robust saliency feature learning by exploiting cross-modal commonality via a Siamese network, while the DCF module is introduced for complementary feature discovery.
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STMTrack: Template-free Visual Tracking with Space-time Memory Networks
TL;DR: A novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking is proposed.
Proceedings ArticleDOI
MixFormer: End-to-End Tracking with Iterative Mixed Attention
TL;DR: This paper proposes a compact tracking framework, termed as MixFormer, built upon transformers, to utilize the flexibility of attention operations, and proposes a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
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