Visual tracking based on adaptive interacting multiple model particle filter by fusing multiples cues

Dhassi, Younes; Aarab, Abdellah

doi:10.1007/s11042-018-5852-5

Cited by 12 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…"Visual tracking based on adaptive interacting multiple model particle filter by fusing multiples cues." [74] 2018 This paper presents a robust tracking system based on the fusion of a particle filter (PF) with interacting multiple models (IMM) to overcome various severe challenges, such as camera motion, fast motion, background clutter, or target appearance changes.…”

Section: Slmentioning

confidence: 99%

A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

Awal,

Refat,

Naznin

et al. 2023

IJACSA

View full text Add to dashboard Cite

Visual object tracking is a crucial research area in computer vision because it can simulate a dynamic environment with non-linear motions and multi-modal non-Gaussian noises. However, This paper presents an overview of the recent developments in particle filter-based visual object tracking algorithms and discusses the pros and cons of particle filters, respectively. There are presentations of many different methodologies and algorithms in the research literature. The majority of visual object tracking research at present is on particle filters. In addition, the most advanced technique for visual object tracking has also been developed by combining the convolutional neural network (CNN) and the particle filter. The advantage of particle filters is that they can handle nonlinear models and non-Gaussian advancements, sequentially concentrating on the areas of the state space with higher densities, primarily parallelization, and simplicity of implementation. Despite this, it offers a robust framework for visual object tracking because it incorporates uncertainty and outperforms other filters like the Kalman filter, Kernelized correlation filter, optical filter, mean shift filter, and extended Kalman filter in recognition tests. In contrast, this study provided information on various particle filter features and classifiers.

show abstract

Section: Slmentioning

confidence: 99%

A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

Awal,

Refat,

Naznin

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Their algorithm tracked multiple targets robustly by using targets' appearance, motion, and interactions. Dhassi and Aarab [4] proposed a visual tracker fusing multiples cues. They fused the color and texture features to describe the appearance of the object under the particle filter framework.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the baseline multi-cue/multi-feature tracking procedure [4,19], the proposed algorithm only uses the dominant feature of a part as the cue for tracking, which aims to overcome the problem that the fusing of multiple features reduces the tracking precision.…”

Section: Dominant Feature For Trackingmentioning

confidence: 99%

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

2021

IAJIT

View full text Add to dashboard Cite

To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking

show abstract

“…In [ 31 ], first, the dynamic behavior of the tracking model was assumed to be linear, which was used to model the motion of the objects using the parametric single acceleration method. The two sub-model states are estimated using an H filter.…”

Section: Introductionmentioning

confidence: 99%

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Masood

Zafar

Ali

et al. 2022

Sensors

View full text Add to dashboard Cite

Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.

show abstract

Visual tracking based on adaptive interacting multiple model particle filter by fusing multiples cues

Cited by 12 publications

References 39 publications

A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Contact Info

Product

Resources

About