Target tracking for a mobile robot with a stereo camera considering illumination changes

Isobe, Yuzuka; Masuyama, Gakuto; Umeda, Kazunori

doi:10.1109/sii.2015.7405065

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Furthermore, it reduces estimation errors regarding a target's position during occlusion. Also in the method of [5], a target is identified from the candidates using a combination of both color and location models based on illumination parameters.…”

Section: Our Previous Methodsmentioning

confidence: 99%

“…In our previous system [5], the segmentation method [19] was applied for candidate extraction. The method utilized an overlooked plane, and 3D information in each pixel of a disparity image was projected onto the plane.…”

Section: Candidate Extractionmentioning

confidence: 99%

“…As with our previous method [5], the total dissimilarity between candidates' features and the model is calculated by combining these dissimilarities in accordance with the illumination changes. The total dissimilarity is defined as follows:…”

Section: Target Identificationmentioning

confidence: 99%

“…Method II is our previous method [5], with procedures for detecting occlusions and both updating the color model and continuing the duration of estimation of target's position. Method III is also our previous method [18] without any procedures for detecting or handling occlusions.…”

Section: Off-line Experimentsmentioning

confidence: 99%

“…One strategy for coping with the problem is to use both the color and location features of a target as humans do. Our previous method [5] was also based on the combination of both features. In that method, a parameter was adopted to show how illumination conditions change.…”

Section: Introductionmentioning

confidence: 99%

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Occlusion handling for a target-tracking robot with a stereo camera

2018

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This paper presents an occlusion-handling method for a target-tracking robot with a stereo camera. One of the main challenges with the robot is to continue tracking when the illumination changes and occlusion occurs. In order to cope with the challenge, we use both color and disparity images acquired from a stereo camera. The tracking system is composed of three phases: candidate extraction, target identification, and occlusion handling. First, by using only three-dimensional (3D) information, target candidates are extracted. Second, the target is identified from the candidates based on a combination of both color and location features of the target and candidates. The combination depends on illumination changes that are supposed by changes in the white balance. Finally, the state of occlusion is estimated by results of both the analysis of the positional relationship between the candidates and the identification of a target. The proper procedure for the state is implemented. In the off-line experiments, the proposed method is compared with previous methods. Then, the proposed method is applied to a mobile robot, and an on-line experiment is carried out. Through the experiments, the effectiveness of the proposed method is verified.

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Section: Our Previous Methodsmentioning

confidence: 99%

Section: Candidate Extractionmentioning

confidence: 99%

Section: Target Identificationmentioning

confidence: 99%

Section: Off-line Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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