GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Musa, Zalili; Salleh, Mohd Zuki; Bakar, Rohani Abu; Watada, Junzo

doi:10.1109/tcsvt.2015.2433172

Cited by 14 publications

(5 citation statements)

References 55 publications

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“…Therefore, different approaches are used to obtain a high-resolution 180 • image as a basis [25,27,30] and specialized tracking algorithms track the game action [24,33]. These methods are also used in other sports such as table tennis [22], tennis [9], or field hockey [23]. The methods and approaches listed have automated various subtasks in video production, but investigations of human characteristics in image editing and individual characteristics of professional vision mixers have not been incorporated, as they are used already for a longer time in audio.…”

Section: Related Workmentioning

confidence: 99%

Modeling of an Automatic Vision Mixer With Human Characteristics for Multi-Camera Theater Recordings

et al. 2023

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Laymen are not trained in camera control and how to use a vision mixing desk to switch between different cameras in a video production. Specific training and expert knowledge are required. In the non-professional environment, multi-camera recordings of theater performances or other stage performances are therefore difficult to realize. This can be remedied by a production process with highresolution cameras from which image sections of long shots or medium long shots are cropped manually or automatically in post-production. One approach to automate the generation of suitable image cuts could be to focus on speaker changes so that the person who is speaking is shown in the generated cut. However, these image cuts can appear static and robotic if they are set too precisely. Therefore, the characteristics and habits of professional vision mixers (persons who operate the vision mixing desk) during the editing process are investigated in more detail in order to incorporate them into an automation process. The characteristic features of five different vision mixers are examined, which were used under almost identical recording conditions for theatrical cuts in TV productions. The cuts are examined with regard to their temporal position in relation to pauses in speech, which take place during speaker changes on stage. It is shown that different professional vision mixers set the cuts individually differently before, in or after the pauses in speech. From the analysis of the image cuts, an approach for a model is developed in which the individual characteristics of a vision mixer can be set. With the help of this model, a more human appearance can be given to otherwise exact and robotic cuts, when automating image cuts. INDEX TERMS Automatic vision mixer, human charakteristics, multi-camera theatre recordingsPreliminary note:The term vision mixer is usually referred to both the device used to edit video and the person operating the device. The ambiguity is avoided in this paper by using the following terms:• Vision mixing desk = the device used to edit video.• Vision mixer (also abbreviated as VM) = the person who operates the vision mixing desk.

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Section: Related Workmentioning

confidence: 99%

Modeling of an Automatic Vision Mixer With Human Characteristics for Multi-Camera Theater Recordings

et al. 2023

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“…Different approaches are used to obtain a high-resolution 180°image as a basis [25,26,27] and specialized tracking algorithms track the game action [28,29]. These methods are also used in other sports such as table tennis [30], tennis [31], or field hockey [32].…”

Section: Related Workmentioning

confidence: 99%

Automatic Camera Selection, Shot Size, and Video Editing in Theater Multi-Camera Recordings

Stoll,

Breide,

Göring

et al. 2023

IEEE Access

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In a non-professional environment, multi-camera recordings of theater performances or other stage shows are difficult to realize, because amateurs are usually untrained in camera work and in using a vision mixing desk that mixes multiple cameras. This can be remedied by a production process with high-resolution cameras where recordings of image sections from long shots or medium-long shots are manually or automatically cropped in post-production. For this purpose, Gandhi et al. presented a singlecamera system (referred to as Gandhi Recording System in the paper) that obtains close-ups from a highresolution recording from the central perspective. The proposed system in this paper referred to as "Proposed Recording System" extends the method to four perspectives based on a Reference Recording System from professional TV theater recordings from the Ohnsorg Theater. Rules for camera selection, image cropping, and montage are derived from the Reference Recording System in this paper. For this purpose, body and pose recognition software is used and the stage action is reconstructed from the recordings into the stage set. Speakers are recognized by detecting lip movements and speaker changes are identified using audio diarization software. The Proposed Recording System proposed in this paper is practically instantiated on a school theater recording made by laymen using four 4K cameras. An automatic editing script is generated that outputs a montage of a scene. The principles can also be adapted for other recording situations with an audience, such as lectures, interviews, discussions, talk shows, gala events, award ceremonies, and the like. Test persons confirm in an online study the added value of the perspective diversity of four cameras of the Proposed Recording System versus the single-camera method of Gandhi et al. INDEX TERMSMulti-camera theater recordings, cropping, automatic montage, 4K, automatic video editing

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“…Table 1 and 2 compare the proposed method with the three existing methods under the single and multi layer neural models. The methods adopted for comparison are adaptive Levenberg-Marquardt algorithm (aLM) [20], hybrid Genetic Algorithm (hGA) [21] and GbLN PSO algorithm (GPSO) [22]. As is seen in Table 1, the single layer the proposed method gives the first best result while comparing with the existing algorithms.…”

Section: Impact Of Model Architecturementioning

confidence: 99%

Extending the Neural Model to Study the Impact of Effective Area of Optical Fiber on Laser Intensity

Bhatnagar¹,

Gupta²

2017

IJIES

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Abstract:Our previous article has proposed a simulation model to explore the nonlinear relationship between the nonlinear regime of the fiber, laser intensity and the error probability of the optical link. This paper extends the model to include the effective area of the fiber. In order to handle the extended parameter, time series feed forward neural network based prediction model is proposed. The reliability of the model is substantiated by extensive experimental analysis through which the relationship exhibiting between the laser intensity and the corresponding nonlinear regime and effective area of the fiber can be observed. The observed results demonstrate the performance of the prediction model in estimating the launch power to meet the desired error probability under varying nonlinear regime and effective of the fiber.

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GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Cited by 14 publications

References 55 publications

Modeling of an Automatic Vision Mixer With Human Characteristics for Multi-Camera Theater Recordings

Modeling of an Automatic Vision Mixer With Human Characteristics for Multi-Camera Theater Recordings

Automatic Camera Selection, Shot Size, and Video Editing in Theater Multi-Camera Recordings

Extending the Neural Model to Study the Impact of Effective Area of Optical Fiber on Laser Intensity

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