Abstract
In this paper, a MDSPF method is proposed to learn a robust observation model for representing the targets by training a CNN with a number of video sequences. The CNN architecture is composed of three shared convolutional units, two shared fully connected (Fc) units and a multiple domain Fc unit, and it is offline trained by a multi-domain learning strategy. After training, the shared convolutional units are remained as an observation model for our tracking framework. The features from the shared convolutional units can well adapt to the challenges in tracking tasks. A scale-adaptive particle filter is also proposed in our framework to improve the robustness of particle filter method. Different from most existing particle filter tackers, it can efficiently shepherd each particle towards a more precise location and scale through similarity evaluation. Extensive experiments are conducted on Object Tracking Benchmark (OTB), UAV123 and LaSOT datasets to verify the efficiency of our proposed method.
Highlights
Visual tracking is considered the problem to estimate the location, shape, motion trajectory, and the size of a target in the coming sequences while only its initial state at the first sequence is given [1]–[4]
The overall plots are obtained with a varying threshold values, and the performances of different tracking methods are ranked based on the area under curve (AUC) score for successful evaluation and the precision score over a certain threshold for precise evaluation
To build a robust online tracking framework for visual tracking tasks, this paper presents a novel tracking method based on deep network and particle filter
Summary
Visual tracking is considered the problem to estimate the location, shape, motion trajectory, and the size of a target in the coming sequences while only its initial state at the first sequence is given [1]–[4]. The generative methods learn a statistical model to describe. The associate editor coordinating the review of this manuscript and approving it for publication was Guitao Cao. the target appearance, and targets are located through generative processes [10]. Incremental learning tracker (IVT) [11] and L1 tracker (L1T) [12] are two popular generative trackers, the former applies principal component analysis (PCA) to represent a target, while the latter represents a target by a sparse combination of over-complete basis vectors. The discriminative methods regard tracking as a binary classification to distinguish target from background, and they have become popular for their good performance [13]. Some works are extended based on the above methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
