Structured Group Local Sparse Tracker
Sparse representation is considered as a viable solution to visual tracking. In this paper, we propose a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularization term. To solve the optimization model, we propose an efficient numerical algorithm consisting of two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmarks of challenging image sequences demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.
Code (0)
등록된 구현이 없습니다.
Tasks
Visual TrackingSimilar Papers 제목 키워드 기반
Robust Structured Group Local Sparse Tracker Using Deep Features
Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking r…
Visual TrackingRobust Structured Multi-task Multi-view Sparse Tracking
Sparse representation is a viable solution to visual tracking. In this paper, we propose a structured multi-task multi-view tracking (SMTMVT) method, which exploits the sparse appearance model in the particle filter fram…
Visual TrackingStructural Sparse Tracking
Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only conside…
Visual TrackingDifferentiable Sparsity via $D$-Gating: Simple and Versatile Structured Penalization
Structured sparsity regularization offers a principled way to compact neural networks, but its non-differentiability breaks compatibility with conventional stochastic gradient descent and requires either specialized opti…
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?
Recently, sparse representation based visual tracking methods have attracted increasing attention in the computer vision community. Although achieve superior performance to traditional tracking methods, however, a basic …
Visual Tracking