uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

Razzaq, Muhammad Asif; Medina-Quero, Javier; Cleland, Ian; Nugent, Chris; Akhtar, Usman; Bilal, Hafiz Syed Muhammad; Rehman, Ubaid ur; Lee, Sungyoung

doi:10.1007/s00530-020-00664-7

Cited by 11 publications

(8 citation statements)

References 53 publications

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“…e full convolutional twin network built by Cai et al is to input the first image of the sports video into one neural network and then input the image of the target to be tracked into another neural network to obtain the possibility that the target is at a certain location and then get the specific location of the target [12]. Razzaq et al used a set of image sequences with time-varying illumination and constant scene reflection coefficients, first performed median filtering on the image sequences according to the time variation, and integrated the results of the median filtering to obtain an Eigen reflected an image with the shadow removal effect [13]. Lu et al automatically detected and removed shadows from a single image to obtain a good removal effect.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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The effect is tested in various specific scenes of sports videos to complete the multitarget motion multitarget tracking detection application applicable to various specific scenes within sports videos. In this paper, deep neural networks are applied to sports video multitarget motion shadow suppression and accurate tracking to improve tracking performance. After the target frame selection is determined, the tracker uses an optical flow method to estimate the limits of the target sports video multitarget motion based on the sports video multitarget motion of the target object between frames. The detector first scans each sports video image frame one by one, observing the previously discovered and learned image frame subregions one by one until the current moment that is highly like the target to be tracked. The preprocessed remote sensing images are converted into grayscale images, the histogram is normalized, and the appropriate height threshold is selected in combination with the regional growth function to realize the rejection of sports video multitarget motion shadow and establish the sports video multitarget network model. The distance and direction of the precise target displacement are determined by frequency-domain vectors and null domain vectors, and the target action judgment mechanism is formed by decision learning. Finally, comparing with other shadow rejection and precision tracking algorithms, the proposed algorithm achieves greater advantages in terms of accuracy and time consumption.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Duan

2021

Complexity

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“…The object tracking algorithms based on CF [6,7] convert the tracking operations into the frequency domain, which can reduce the amount of calculation and ensure data integrity. Many domestic and foreign scholars have introduced CF into object tracking algorithms IASC, 2023 3 [8,9] and achieved excellent results in the latest open datasets and academic competitions. The trackers based on CF only need to extract features from the searching box once and generate many candidate samples through cyclic convolution operation.…”

Section: Tracking By Correlation Filtersmentioning

confidence: 99%

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Sun¹,

Li²,

Cheng³

2023

Intelligent Automation &Amp; Soft Computing

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Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion, a tracking algorithm based on multi-time-space perception and instancespecific proposals is proposed to optimize the mathematical model of the correlation filter (CF). Firstly, according to the consistency of the changes between the object frames and the filter frames, the mask matrix is introduced into the objective function of the filter, so as to extract the spatio-temporal information of the object with background awareness. Secondly, the object function of multi-feature fusion is constructed for the object location, which is optimized by the Lagrange method and solved by closed iteration. In the process of filter optimization, the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final tracking results. Finally, when the tracking results fluctuate, the boundary suppression factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks, the object tracking benchmark 2015 (OTB2015) and the temple color 128 (TC-128). Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective. The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark, which are better than the results of other competing algorithms. The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.

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“…Razzaq et al proposed a dynamic cluster membership selection algorithm for multitarget tracking and constructed a comprehensive performance metric function based on network energy consumption and tracking accuracy, which can measure WSN tracking performance to some extent [5]. However, the algorithm does not consider the energy consumption of individual nodes, the residual energy, and the contribution of the selected nodes' measurement information to target localization.…”

Section: Related Jobsmentioning

confidence: 99%

Distributed Soccer Training Smart Sensors for Multitarget Localization and Tracking

Jiang

Qiu

2022

Journal of Sensors

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This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.

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uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

Cited by 11 publications

References 53 publications

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

[Retracted] Deep Learning‐Based Multitarget Motion Shadow Rejection and Accurate Tracking for Sports Video

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Distributed Soccer Training Smart Sensors for Multitarget Localization and Tracking

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