Learning spatially correlation filters based on convolutional features via PSO algorithm and two combined color spaces for visual tracking

Touil, Djamel Eddine; Terki, Nadjiba; Medouakh, Saadia

doi:10.1007/s10489-017-1120-z

Cited by 16 publications

(3 citation statements)

References 37 publications

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“…Reference [ 18 ] proposes a rotation-invariant feature transformation, which divides each standard circle into a series of concentric circles, and each concentric circle is associated with a gradient orientation histogram. Reference [ 19 ] proposed that the gradient position and orientation histogram is an extension of the SIFT descriptor. Similar to PCA-SIFT, GLOH also reduces the dimensionality of descriptors through principal component analysis.…”

Section: Related Workmentioning

confidence: 99%

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Zhang

Yang

2022

Computational Intelligence and Neuroscience

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Eye tracking is currently a research hotspot in the territory of service robotics. There is an urgent need for machine vision technique in the territory of video surveillance, and biological visual object following is one of the important basic research problems. By tracking the object of interest and recording the tracking trajectory, we can extract a structure from a video. It can also analyze the abnormal behavior of groups or individuals in the video or assist the public security organs in inquiring and searching for evidence of criminal suspects, etc. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following in visual surveillance is easily affected by factors such as occlusion, rapid object movement, and appearance changes, and it is difficult to solve these problems effectively with single-layer features. This paper adopts a visual object following algorithm based on visual information features and few-shot learning, which effectively improves the accuracy and robustness of tracking.

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Section: Related Workmentioning

confidence: 99%

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Zhang

Yang

2022

Computational Intelligence and Neuroscience

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“…Extracting a robust target representation is the critical component of state-of-the-art visual tracking methods to overcome these challenges. Hence, to robustly model target appearance, these methods utilize a wide range of handcrafted features (e.g., [41,7,87,55,92,68] which exploit histogram of oriented gradients (HOG) [11], histogram of local intensities (HOI), and Color Names (CN) [81]), deep features from deep neural networks (e.g., [61,63,85,2,79,40], or both (e.g., [14,16,64]).…”

Section: Take Down Policymentioning

confidence: 99%

Effective fusion of deep multitasking representations for robust visual tracking

et al. 2021

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“…• Based on the importance of combining feature representations from different CNN layers [3,4,24], a model of Hierarchical Convolutional Filters (HCF) is proposed. The proposed model is composed of different convolutional layers (conv1-4, conv3-4, conv4-4, and conv5-4).…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Visual Object Tracking Approach based on Combined Features of Neural Networks, Wavelet Transforms, and Histogram of Oriented Gradients

Bourennane

Terki

Hamiane

et al. 2022

Eng. Technol. Appl. Sci. Res.

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In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main problem the existing approaches encounter, i.e. the significant appearance changes which are mainly caused by heavy occlusion and illumination variation. The proposed approach is based on a combination of Deep Convolutional Neural Networks (DCNNs), Histogram of Oriented Gradient (HOG) features, and discrete wavelet packet transforms. The problem of illumination variation is solved by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transforms are used at the output for extracting the CNN features. By combining four learned correlation filters with the convolutional features, the target location is deduced using multichannel correlation maps at the CNN output. On the other side, the maximum value of the resulting maps from the correlation filters with convolutional features produced by the previously obtained HOG feature of the image template are calculated and are used as an updating parameter of the correlation filters extracted from CNN and from HOG. The major aim is to ensure long-term memory of the target appearance so that the target item may be recovered if tracking fails. In order to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. The obtained results demonstrate the superiority of the proposed approach.

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Learning spatially correlation filters based on convolutional features via PSO algorithm and two combined color spaces for visual tracking

Cited by 16 publications

References 37 publications

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Effective fusion of deep multitasking representations for robust visual tracking

An Enhanced Visual Object Tracking Approach based on Combined Features of Neural Networks, Wavelet Transforms, and Histogram of Oriented Gradients

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