Modality-correlation-aware sparse representation for RGB-infrared object tracking

Lan, Xiangyuan; Ye, Mang; Zhang, Shengping; Zhou, Huiyu; Yuen, Pong C.

doi:10.1016/j.patrec.2018.10.002

Cited by 95 publications

(29 citation statements)

References 80 publications

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“…Such a classification strategy can reduce negative effects brought by "bad" local regions (i.e., regions with local corruptions, occlusions, or deformations) in the test sample. The effectiveness of the idea of multi-dictionary has also been corroborated in other research fields, such as visual tracking [60][61][62][63].…”

Section: Our Motivations and Contributionsmentioning

confidence: 69%

Multi‐Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

Zhang

Zhu

2019

Mathematical Problems in Engineering

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Biometrics based personal authentication has been found to be an effective method for recognizing, with high confidence, a person’s identity. With the emergence of reliable and inexpensive 3D scanners, recent years have witnessed a growing interest in developing 3D biometrics systems. As a commonsense, matching algorithms are crucial for such systems. In this paper, we focus on investigating identification methods for two specific 3D biometric identifiers, 3D ear and 3D palmprint. Specifically, we propose a Multi-Dictionary based Collaborative Representation (MDCR) framework for classification, which can reduce the negative effects aroused by some local regions. With MDCR, a range map is partitioned into overlapping blocks and, from each block, a feature vector is extracted. At the dictionary construction stage, feature vectors from blocks having the same locations in gallery samples can form a dictionary and, accordingly, multiple dictionaries are obtained. Given a probe sample, by coding its each feature vector on the corresponding dictionary, multiple class labels can be obtained and then we use a simple majority-based voting scheme to make the final decision. In addition, a novel patch-wise and statistics-based feature extraction scheme is proposed, combining the range image’s local surface type information and local dominant orientation information. The effectiveness of the proposed approach has been corroborated by extensive experiments conducted on two large-scale and widely-used benchmark datasets, the UND Collection J2 3D ear dataset and the PolyU 3D palmprint dataset. To make the results reproducible, we have publicly released the source code.

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Section: Our Motivations and Contributionsmentioning

confidence: 69%

Multi‐Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

Zhang

Zhu

2019

Mathematical Problems in Engineering

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“…In [34], confidence maps from RGB and infrared modalities were aggregated for pedestrian tracking by using sum rule based on a probabilistic background model. To enhance the tracking robustness, several sparse representationbased trackers are developed in which some fusion models such as feature concatenation [35], group sparsity [37], low rank regularization [45] were exploited for modality combination. However, these methods may fail to effectively and jointly utilize the modality consistency and specificity.…”

Section: A Rgb-infrared Trackingmentioning

confidence: 99%

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

Lan

Shao

et al. 2019

IEEE Trans. Ind. Electron.

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116

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With a large number of video surveillance systems installed for the requirement from industrial security, the task of object tracking, which aims to locate objects of interest in videos, is very important. Although numerous tracking algorithms for RGB videos have been developed in the decade, the tracking performance and robustness of these systems may be degraded dramatically when the information from RGB video is unreliable (e.g. poor illumination conditions or very low resolution). To address this issue, this paper presents a new tracking system which aims to combine the information from RGB and infrared modalities for object tracking. The proposed tracking systems is based on our proposed machine learning model. Particularly, the learning model can alleviate the modality discrepancy issue under the proposed modality consistency constraint from both representation patterns and discriminability, and generate discriminative feature templates for collaborative representations and discrimination in heterogeneous modalities. Experiments on a variety of challenging RGB-infrared videos demonstrate the effectiveness of the proposed algorithm.

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“…Generally, most of existing tracking methods falls into either the non-CNN-based category or the CNN-based category according to whether CNN features are used. The non-CNN-based tracking methods usually employ the sparse coding framework to obtain effective image representations [17,[33][34][35][36][37][38]. In [17], spatial structure among selected local templates are enhanced to exclude distractors introduced by noisy templates.…”

Section: Related Workmentioning

confidence: 99%

Robust visual tracking via samples ranking

Zhu

Wang

2019

EURASIP J. Adv. Signal Process.

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In recent years, deep convolutional neural networks (CNNs) have achieved great success in visual tracking. To learn discriminative representations, most of existing methods utilize information of image region category, namely target or background, and/or of target motion among consecutive frames. Although these methods demonstrated to be effective, they ignore the importance of the ranking relationship among samples, which is able to distinguish one positive sample better than another positive one or not. This is especially crucial for visual tracking because there is only one best target candidate among all positive candidates, which tightly bounds the target. In this paper, we propose to take advantage of the ranking relationship among positive samples to learn more discriminative features so as to distinguish closely similar target candidates. In addition, we also propose to make use of the normalized spatial location information to distinguish spatially neighboring candidates. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against several state-of-the-art methods.

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Modality-correlation-aware sparse representation for RGB-infrared object tracking

Cited by 95 publications

References 80 publications

Multi‐Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

Multi‐Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

Learning Modality-Consistency Feature Templates: A Robust RGB-Infrared Tracking System

Robust visual tracking via samples ranking

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