Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method

Javanmardi, Mohammadreza; Qi, Xiaojun

doi:10.1007/s00138-018-0963-6

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…In recent works, labeled RFSs have been considered in different visual MTT applications. The authors in [20] have applied labeled RFSs on the tracking of resident space objects (RSOs) via SMC-based multi-Bernoulli filtering framework by assuming that RSOs normally have a few pixels of images in size and they do not have any significant position changes between two consecutive frames. We will see that the position changes of targets are much higher in our case considering the size of FoV, number of pixels, and gait velocity of moving users.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Rabiee

Karlsson

2021

Remote Sensing

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Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.

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Section: Related Work and Contributionmentioning

confidence: 99%

Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Rabiee

Karlsson

2021

Remote Sensing

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“…It has been considered as one of the most paramount and challenging topics in computer vision with various applications in human motion analysis, surveillance, smart vehicles transportation, navigation, etc. Although numerous tracking methods [1–8] have been introduced in the recent years, developing a robust algorithm that can handle different challenges such as occlusion (OCC), illumination variations (IVs), deformation (DEF), fast motion (FM), camera motion, and background clutter (BC) still remains unsolved.…”

Section: Introductionmentioning

confidence: 99%

Structured group local sparse tracker

Javanmardi¹,

Qi²

2019

IET Image Processing

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Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking result. In this paper, we propose a robust deep features-based structured group local sparse tracker (DF-SGLST), which exploits the deep features of local patches inside target candidates and represents them by a set of templates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in DF-SGLST employs a group-sparsity regularization term to seamlessly adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the closed-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers. Precision Precision plots of OPE -out of view (6) TGPR_HOG [0.785] MEEM [0.727] RSST_Deep [0.688] SRDCF [0.680] STAPLE [0.679] KCF [0.650] SMTMVT [0.642] SAMF [0.636] MTMVTLAD [0.607] RSST_HOG [0.600] DF-SGLST [0.588] TLD [0.576] MTMVTLS [0.573] LOT [0.567] Struck [0.539] LSK [0.515] DSST [0.511] CXT [0.510] TM-V [0.502] CNT [0.502] Fig. 3: The OPE success plots and precision plots for each of 11 challenge subsets in OTB50. Precision Precision plots of OPE -deformation (44) HDT [0.821] CNN-SVM [0.793] CF2 [0.791] MEEM [0.754] STAPLE [0.748] SRDCF [0.734] DF-SGLST [0.727] DLSSVM [0.727] RSST_Deep [0.717] LCT [0.689] SAMF [0.682] SGLST_HOG [0.628] TGPR_HOG [0.624] KCF [0.617] RSST_HOG [0.594] DSST [0.550] SGLST_Color [0.543] SCM [0.537] Struck [0.527] CPF [0.508]

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