Multiple objects tracking by a highly decisive three-frame differencing-combined-background subtraction method with GMPFM-GMPHD filters and VGG16-LSTM classifier

Chandrasekar, K.; Geetha, P.

doi:10.1016/j.jvcir.2020.102905

Cited by 14 publications

(13 citation statements)

References 41 publications

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“…The techniques considered for the assessment include UKF, 21 TPM, 20 TGCN, 16 distractor‐aware discriminative model, 23 three‐frame differencing‐combined‐background subtraction (TFDCBS), 22 granulated RCNN and multi‐class deep SORT (G‐RCNN + MCD‐SORT), 12 Jaya‐PO‐based DCNN + HGSO‐based UKF without clustering, DCNN + HGSO‐based UKF, Jaya‐PO‐based DCNN + UKF, and proposed Jaya‐PO‐based DCNN + HGSO‐based UKF.…”

Section: Discussing With Resultsmentioning

confidence: 99%

“…However, this technique failed to adapt improper edifice of short tracklets to yield better performance. Chandrasekar and Geetha 22 devised a three‐frame differencing combined‐background subtraction (TFDCBS) and fast histogram‐entropy‐based thresholding (HEBT) method for tracking different objects from videos. The method detected the objects using 3D bounding boxes to find different features from images.…”

Section: Motivationmentioning

confidence: 99%

“…The problems faced by multi‐object tracking methods are described below, TFDCBS is developed for tracking multiple objects, but this technique failed to track moving objects rapidly by estimating speed and count 22 In Reference 20, TPM is devised for tracking multiple objects, but this technique failed to adapt representative features for reducing errors while developing short tracklets. An online multi‐object tracking model on the basis of a hierarchical single‐branch network is developed for tracking multiple objects, but the major issue relies on enhancing detection quality and fails to optimize tracking 24 …”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

Jaganathan¹,

Panneerselvam

Kumaraswamy³

2022

Concurrency and Computation

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Summary The tracking of multiple objects has gained immense interest in several applications that ranges from video surveillance and robotics. The goal of tracking multiple objects is to discover correspondences and perform matching among various objects in frames. However, occlusion is a major issue in several classical techniques, which fail to track multiple moving objects. Here, the extraction of frames is done to obtain information about an action. Thereafter, the segmentation of objects is done using the entropy weighting K‐means (EWKM) algorithm to generate optimum segments from the frame. After that, the detection of the object is done using a deep convolutional neural network (DCNN) to obtain objects from segments. Proposed Jaya‐political optimizer (Jaya‐PO) is used to train DCNN, which is devised by collaborating political optimizer (PO) and Jaya optimization algorithm. After detecting the object, the tracking of multiple objects is done using the proposed Henry gas solubility optimized unscented Kalman filtering (HGSO‐based UKF), wherein the UKF is modified with HGSO. The proposed Jaya‐PO‐based DCNN and HGSO‐based UKF outperformed other methods with high accuracy of 92%, high multiple object tracking precision (MOTP) of 87.6%, high sensitivity of 91.7%, and high specificity of 92%.

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Section: Discussing With Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

Jaganathan¹,

Panneerselvam

Kumaraswamy³

2022

Concurrency and Computation

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“…classify) the objects, and locating the recognized objects in the input image [13]. The current object detection methods can be divided into five main categories based on image segmentation [14]- [15], template matching [16], optical flow [17]- [18], frame difference [19]- [20], and machine learning (including both traditional machine learning and deep learning). Traditional methods are difficult to manually design features, and too much useful information will be lost in feature extraction [21].…”

Section: B Object Detection Technologymentioning

confidence: 99%

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

Zhang

et al. 2021

IEEE Access

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The electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact of the bucket teeth with hard and abrasive materials such as ore during the process of the mining excavation can cause the bucket teeth to break and fall off prematurely, resulting in unplanned downtime and productivity losses. In response to this problem, we have developed a visionbased bucket teeth fault detection algorithm with deep learning. Using a dataset based on the images of both real shovel teeth and 3D-printed models, we trained a Faster Region Convolutional Neural Network (Faster R-CNN) to obtain the number of normal bucket teeth and the positions of the bucket teeth from the images, using the additional bucket dataset from 3D-printed models to pre-train the network for improving its detection accuracy on the real bucket data. We compared the resulting Faster R-CNN model with the ZFNet, the ResNet-50, and the VGG16 and found our Faster R-CNN model to perform best in terms of accuracy and speed.

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“…In this research, AC, SE, SP, and F1 are calculated for the comparing analysis. ANN [37], k-nearest neighbor (kNN) [38], Fast region-based convolutional neural network (Fast R-CNN) [39], Visual Geometry Group -16 (VGG16) [40], Scaled Conjugate Gradient CNN (SGC-CNN) [41], GoogleNet [42], AlexNet [43], ResNet-50-177 [44], and Inception-v3 [45] were selected for comparing analysis finally. The proposed model's performance is listed in Table 2; and the results show that the dominance of the proposed improved resident network-based cGAN model (RNcGAN) in indices of A, SE, SP, and F1.…”

Section: Fig 9 Plantar Pressure Experimental Data-set Collection Anmentioning

confidence: 99%

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

Han

Wang

et al. 2021

Preprint

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The number of layers of deep learning (DL) increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based DNN model was applied to address these issues recently. This paper improved the RN and developed a rectified linear unit (ReLU) based conditional generative adversarial nets (cGAN) to classify plantar pressure images. A foot scan system collected the plantar pressure images, in which normal (N), planus (PL), and talipes equinovarus feet (TE) data-sets were acquired subsequently. The 9-foot types named N, PL, TE, N-PL, N-TE, PL-N, PL-TE, TE-N, and TE-PL were classified using the proposed DNN models, named resident network-based conditional generative adversarial nets (RNcGAN). It improved the RN structure firstly and the cGAN system hereafter. In the classification of plantar pressure images, the pixel-level state matrix can be direct as an input, which is different from the previous image classification task with image reduction and feature extraction. cGAN can directly output the pixels of the image without any simplification. Finally, the model achieved better results in the evaluation indicators of accuracy (AC), sensitivity (SE), and F1-measurement (F1) by comparing to artificial neural networks (ANN), k-nearest neighbor (kNN), Fast Region-based Convolution Neural Network (Fast R-CNN), visual geometry group (VGG16), scaled-conjugate-gradient convolution neural networks (SCG-CNN), GoogleNet, AlexNet, ResNet-50-177, and Inception-v3. The final prediction of class accuracy is 95.17%. Foot type classification is vital for producing comfortable shoes in the industry.

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Multiple objects tracking by a highly decisive three-frame differencing-combined-background subtraction method with GMPFM-GMPHD filters and VGG16-LSTM classifier

Cited by 14 publications

References 41 publications

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

Object detection and multi‐object tracking based on optimized deep convolutional neural network and unscented Kalman filtering

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

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