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LIME: Low-Light Image Enhancement via Illumination Map Estimation

Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.
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Shape Classification Using the Inner-Distance

  • Haibin LingD. Jacobs
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 February 2007
It is suggested that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts.
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LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking

LaSOT is presented, a high-quality benchmark for Large-scale Single Object Tracking that consists of 1,400 sequences with more than 3.5M frames in total, and is the largest, to the best of the authors' knowledge, densely annotated tracking benchmark.
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Real time robust L1 tracker using accelerated proximal gradient approach

This paper proposes an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers and a very fast numerical solver is developed to solve the resulting ℓ1 norm related minimization problem with guaranteed quadratic convergence.
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Encoding color information for visual tracking: Algorithms and benchmark

This paper comprehensively encode 10 chromatic models into 16 carefully selected state-of-the-art visual trackers and performs detailed analysis on several issues, including the behavior of various combinations between color model and visual tracker, the degree of difficulty of each sequence for tracking, and how different challenge factors affect the tracking performance.
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The Sixth Visual Object Tracking VOT2018 Challenge Results

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are…
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Robust Visual Tracking and Vehicle Classification via Sparse Representation

  • Xue MeiHaibin Ling
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 November 2011
This paper proposes a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework and extends the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes.
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Robust Visual Tracking using 1 Minimization

This paper proposes a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework and introduces a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure.

Transfer Learning Based Visual Tracking with Gaussian Processes Regression

This paper directly analyze this probability of target appearance as exponentially related to the confidence of a classifier output using Gaussian Processes Regression (GPR), and introduces a latent variable to assist the tracking decision.
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DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
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