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The Seventh Visual Object Tracking VOT2019 Challenge Results
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-artExpand
RGB-T Object Tracking: Benchmark and Baseline
A novel graph-based approach to learn a robust object representation for RGB-T tracking is proposed, in which the tracked object is represented with a graph with image patches as nodes, dynamically learned in a unified ADMM (alternating direction method of multipliers)-based optimization framework. Expand
Weighted Sparse Representation Regularized Graph Learning for RGB-T Object Tracking
A novel graph model, called weighted sparse representation regularized graph, is proposed to learn a robust object representation using multispectral (RGB and thermal) data for visual tracking, in which the modality weight is introduced to leverage RGB and thermal information adaptively. Expand
Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking
An adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework and jointly optimize sparse codes and the reliable weights of different modalities in an online way to perform robust object tracking in challenging scenarios. Expand
Multi-Adapter RGBT Tracking
A novel Multi-Adapter convolutional Network (MANet) is proposed to jointly perform modality-shared, modalities-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. Expand
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures With Edge-Preserving Coherence
A unified framework called pixelwise image saliency aggregating (PISA) various bottom-up cues and priors is proposed, which generates spatially coherent yet detail-preserving, pixel-accurate, and fine-grained saliency, and overcomes the limitations of previous methods. Expand
Attributes Guided Feature Learning for Vehicle Re-identification
A novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID, and design a view-specified generative adversarial network to generate the multi-view vehicle images. Expand
Grayscale-Thermal Object Tracking via Multitask Laplacian Sparse Representation
Experiments suggest that the proposedgrayscale-thermal object tracking method in Bayesian filtering framework based on multitask Laplacian sparse representation outperforms both grayscale and graysscale-Thermal tracking approaches. Expand
Dense Feature Aggregation and Pruning for RGBT Tracking
This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network that achieves clear state-of-the-art against other RGB and RGBT tracking methods. Expand
SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation
The positive samples generation network (PSGN) is introduced to sampling massive diverse training data through traversing over the constructed target object manifold and generated diverse target object images can enrich the training dataset and enhance the robustness of visual trackers. Expand