1,992 research outputs found
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm
Dichlorido{[2-(diphenylphosphino)phenyliminomethyl]ferrocene-κ2 N,P}platinum(II) dichloromethane hemisolvate
In the title compound, [FePt(C5H5)(C24H19NP)Cl2]·0.5CH2Cl2, the PtII atom adopts a distorted square-planar geometry defined by one P atom and one N atom from the bidentate [2-(diphenylphosphino)phenyliminomethyl]ferrocene ligand and two Cl atoms. Two disordered dichloromethane solvent molecules are each 0.25-occupied on a twofold rotation axis
Zero-Assignment Constraint for Graph Matching with Outliers
Graph matching (GM), as a longstanding problem in computer vision and pattern
recognition, still suffers from numerous cluttered outliers in practical
applications. To address this issue, we present the zero-assignment constraint
(ZAC) for approaching the graph matching problem in the presence of outliers.
The underlying idea is to suppress the matchings of outliers by assigning
zero-valued vectors to the potential outliers in the obtained optimal
correspondence matrix. We provide elaborate theoretical analysis to the
problem, i.e., GM with ZAC, and figure out that the GM problem with and without
outliers are intrinsically different, which enables us to put forward a
sufficient condition to construct valid and reasonable objective function.
Consequently, we design an efficient outlier-robust algorithm to significantly
reduce the incorrect or redundant matchings caused by numerous outliers.
Extensive experiments demonstrate that our method can achieve the
state-of-the-art performance in terms of accuracy and efficiency, especially in
the presence of numerous outliers
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