Robust Object Tracking in Infrared Video via Adaptive Weighted Patches

Wang, Jiangtao; Zhang, Jingai

doi:10.3390/mca22010003

Cited by 3 publications

(1 citation statement)

References 23 publications

(22 reference statements)

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“…Performing object tracking in infrared video has received great interests in recent years [74]. In [75], an adaptive weighted patch-based appearance model is proposed to deal with non-rigid deformation for infrared tracking. Based on background subtraction, a novel multiple-target tracking-before-detection method with δ generalized labeled multi-Bernoulli filter is developed to track the objects as pixel set [76].…”

Section: B Infrared Object Trackingmentioning

confidence: 99%

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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Section: B Infrared Object Trackingmentioning

confidence: 99%

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

Lan

Shao

et al. 2019

IEEE Access

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Internet of things (IoT), cloud computing, and big data collective intelligence for smart cities

Xhafa

et al. 2022

Collective Intelligence for Smart Cities

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Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models

Song

Sun

2017

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Object tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach a classifier with sparse discriminative features to separate the target object from the background based on holistic templates. In the multi-scale local model, the object is represented by multi-scale local sparse representation histograms, which exploit the complementary partial and spatial information of an object across different scales. Finally, a collaborative similarity score of one candidate target is input into a Bayesian inference framework to estimate the target state sequentially during tracking. Experimental results on the various challenging video sequences show that the proposed method performs favorably compared to several state-of-the-art trackers.

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Robust Object Tracking in Infrared Video via Adaptive Weighted Patches

Cited by 3 publications

References 23 publications

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

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

Internet of things (IoT), cloud computing, and big data collective intelligence for smart cities

Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models

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