Multi-Target Tracking

Ristić, Branko

doi:10.1007/978-1-4614-6316-0_6

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The position model aims to locate the target in the image space. There are many position models existed, such as correlation filter, template matching, mean shift [18], Kalman filter [19], particle filter [20] and so on. The focus of this paper is on the scale model, which aims to predict the accurate target scale.…”

Section: Our Tracking Process Flowmentioning

confidence: 99%

Accurate scale estimation for visual tracking with significant deformation

Chu

Yang

2020

IET Computer Vision

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Scale variation of a target frequently appears in tasks of visual tracking. Accurate scale estimation is challenging due to deformation, occlusion, rotation, change in the view angle and diversity of tracking object categories. Most tracking methods employ an exhaustive search of scales to estimate the target scales. However, only finite and discrete scales are usually searched due to the expensive computation requirement. Here, the authors propose a novel scale estimation method based on bounding box regression (BBR). They first formulate the scale tracking as a regression problem, and search for the entire continuous scale space without being limited by a manually specified number of scales. Then they extend the original single‐channel BBR to multi‐channel situations, to allow for better employment of multi‐channel features. To further take advantage of the time prior information of training samples, they derive a time‐related sample weighted multi‐channel BBR. Besides, they propose a quantitative measurement, scale divergence degree, to reflect the diversity of sampling strategy. Experimental results on OTB‐2015D dataset demonstrate that the proposed approach achieves outstanding scale estimation performance for visual tracking with significant deformation.

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Section: Our Tracking Process Flowmentioning

confidence: 99%

Accurate scale estimation for visual tracking with significant deformation

Chu

Yang

2020

IET Computer Vision

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Robot Navigation Model in a Multi-Target Domain Amidst Static and Dynamic Obstacles

Ayomoh¹,

Omotuyi²,

Roux³

et al. 2018

Modelling, Simulation and Identification / 858: Intelligent Systems and Control

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This paper presents an efficient robot navigation model in a multi-target domain amidst static and dynamic workspace obstacles. The problem is that of developing an optimal algorithm to minimize the total travel time of a robot as it visits all target points within its task domain amidst unknown workspace obstacles and finally return to its initial position. In solving this problem, a classical algorithm was first developed to compute the optimal number of paths to be travelled by the robot amidst the network of paths. The principle of shortest distance between robot and targets was used to compute the target point visitation order amidst workspace obstacles. A hybrid modelling scheme premised on geometrical considerations was applied to determine the length of obstacles encountered by the robot. Also, a dynamic model premised on the collapse of the radiation cone was proposed and used to estimate the velocity of a dynamic obstacle along the robot's path.

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Multi-Target Tracking

Cited by 2 publications

References 19 publications

Accurate scale estimation for visual tracking with significant deformation

Accurate scale estimation for visual tracking with significant deformation

Robot Navigation Model in a Multi-Target Domain Amidst Static and Dynamic Obstacles

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