Abstract
Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers.
Highlights
Visual tracking aims to estimate states of a moving object or multiple objects in frame sequences under different conditions
We propose a robust structured tracker using local deep features (STLDF), which exploits the convolutional neural networks (CNNs) features of the local patches inside a target candidate and sparsely represents them in a novel convex optimization model
We evaluate the performance of the proposed STLDF and its two variants, namely, structured tracker using local color features (STLCF) and structured tracker using local histogram of oriented gradient (HOG)
Summary
Visual tracking aims to estimate states of a moving object or multiple objects in frame sequences under different conditions. It has been considered as one of the most active and challenging computer vision topics with a large array of applications in autonomous driving, video content analysis and understanding, surveillance, and so forth. Some improvements have been achieved in several tracking methods [1,2,3,4,5,6], computer vision researchers still aim to develop more robust algorithms capable of handling various challenges including occlusion, illumination variations, in-plane and out-plane rotation, background clutter, deformation, and low resolution. Discriminative tracking methods formulate a binary decision boundary to distinguish the target from backgrounds. Eigenspace learning [10], incremental subspace learning [11], and sparse representation [5,12,13,14]
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