Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction

Pang, Meng; Wang, Binghui; Cheung, Yiu-ming; Chen, Lin

doi:10.1109/access.2017.2730281

Cited by 10 publications

(1 citation statement)

References 46 publications

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“…Developing a reliable object tracker is very important for intelligent video analysis, and it plays the key role in motion perception in videos (Chang et al (2017b,a); Chang and Yang (2017); Li et al (2017b); Ma et al (2018); Wang et al (2017Wang et al ( , 2016b; Luo et al (2017)). While significant progress in object tracking research has been made and many object tracking algorithms have been developed with promising performance (Ye et al (2015(Ye et al ( , 2016(Ye et al ( , 2017(Ye et al ( , 2018b; Zhou et al (2018b,a); Ye et al (2018a); Liu et al (2018); Lan et al (2018a); Zhang et al (2013bZhang et al ( , 2017dZhang et al ( ,c, 2018c; Song et al (2017Song et al ( , 2018; Zhang et al (2017bZhang et al ( , 2016Zhang et al ( , 2018a; Hou et al (2017); Yang et al (2016); Zhong et al (2014); Guo et al (2017); Ding et al (2018); Shao et al (2018); Yang et al (2018b,a); Pang et al (2017)), it is worth noting that most of these trackers are designed for tracking objects in RGB image sequences, in which they model the object's appearance via the visual features extracted from RGB video frames. This may limit them to be employed in real applications, such as tracking objects in a dark environment where * * Corresponding author: mangye@comp.hkbu.edu.hk (Mang Ye) the lighting condition is poor and the RGB information is not reliable.…”

Section: Introductionmentioning

confidence: 99%

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

Lan

Zhang

et al. 2020

Pattern Recognition Letters

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To intelligently analyze and understand video content, a key step is to accurately perceive the motion of the interested objects in videos. To this end, the task of object tracking, which aims to determine the position and status of the interested object in consecutive video frames, is very important, and has received great research interest in the last decade. Although numerous algorithms have been proposed for object tracking in RGB videos, most of them may fail to track the object when the information from the RGB video is not reliable (e.g. in dim environment or large illumination change). To address this issue, with the popularity of dual-camera systems for capturing RGB and infrared videos, this paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking. Specifically, this proposed model is able to 1) perform feature representation of objects in different modalities by employing the robustness of sparse representation, and 2) combine the representation by exploiting the modality correlation. Extensive experiments demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

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

Lan

Zhang

et al. 2020

Pattern Recognition Letters

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Robust heterogeneous discriminative analysis for face recognition with single sample per person

Pang

Cheung

Wang

et al. 2019

Pattern Recognition

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Online Non-Negative Multi-Modality Feature Template Learning for RGB-Assisted Infrared Tracking

Lan

Shao

et al. 2019

IEEE Access

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Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.INDEX TERMS Optical image processing, sensor fusion, computer vision.

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Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction

Cited by 10 publications

References 46 publications

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

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

Robust heterogeneous discriminative analysis for face recognition with single sample per person

Online Non-Negative Multi-Modality Feature Template Learning for RGB-Assisted Infrared Tracking

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