Comparison of Statistical and Shape-Based Approaches for Non-rigid Motion Tracking with Missing Data Using a Particle Filter

Abed, Abir El; Dubuisson, Séverine; Béréziat, Dominique

doi:10.1007/11864349_17

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…So, in essence, motion tracking is achieved by tracking the statistical models of foreground (object) and the background in each video frame. This approach is stable in the presence of noise, illumination changes and shadows [25,26,27,28,29,30,31,32,33]. Some approaches employ optical flow to track the apparent motion and then use it to predict the position/pose in the next frames.…”

Section: Motion Tracking Principles In Videosmentioning

confidence: 99%

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Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Abbas

Rodó

2019

Sensors

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Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; (i) it is time-consuming, (ii) it is prone to human errors, and (iii) no two human annotators will 100% agree on annotation, therefore, it is not reproducible. Consequently, automated annotation for such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine learning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of specific laboratory animals, i.e. rodents. We have divided these papers into categories based upon the hardware they use and the software approach they take. We have also summarized their strengths and weaknesses.

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Section: Motion Tracking Principles In Videosmentioning

confidence: 99%

“…Foreground and background pixels are differentiated by comparing pixel statistics with that of background model. This approach is stable in the presence of noise, illumination changes and shadows [11,12,13,14,15,16,17,18,19,20].…”

Section: Statistical Approachesmentioning

confidence: 99%

Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Abbas

Rodó

2019

Sensors

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Bibliography

2015

Tracking With Particle Filter for High‐Dimensional Observation and State Spaces

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ENMIM: Energetic Normalized Mutual Information Model for Online Multiple Object Tracking with Unlearned Motions

Abed

Dubuisson

Béréziat

2007

Advanced Concepts for Intelligent Vision Systems

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International audienceIn multiple-object tracking, the lack in prior information limits the association performance. Furthermore, to improve tracking, dynamic models are needed in order to determine the settings of the estimation algorithm. In case of complex motions, the dynamic cannot be learned and the task of tracking becomes difficult. That is why online spatio-temporal motion estimation is of crucial importance. In this paper, we propose a new model for multiple target online tracking: the Energetic Normalized Mutual Information Model (ENMIM). ENMIM combines two algorithms: (i) Quadtree Normalized Mutual Information, QNMI, a recursive partitioning methodology involving a region motion extraction; (ii) an energy minimization approach for data association adapted to the constraint of lack in prior information about motion and based on geometric properties. ENMIM is able to handle typical problems such as large inter-frame displacements, unlearned motions and noisy images with low contrast. The main advantage of ENMIM is its parameterless and its capacity to handle noisy multi-modal images without exploiting any pre-processing step

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Comparison of Statistical and Shape-Based Approaches for Non-rigid Motion Tracking with Missing Data Using a Particle Filter

Cited by 9 publications

References 7 publications

Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Bibliography

ENMIM: Energetic Normalized Mutual Information Model for Online Multiple Object Tracking with Unlearned Motions

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