Using pano-mapping tables for unwarping of omni-images into panoramic and perspective-view images

Jeng, Sheng-Wen; Tsai, Wen‐Hsiang

doi:10.1049/iet-ipr:20060201

Cited by 24 publications

(24 citation statements)

References 16 publications

(16 reference statements)

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“…Mainly two approaches have been proposed in the literature; 1) application of successful techniques developed under perspective setting and 2) different techniques directly applied for omni-images. A well-known technique in perspective environments can be applied to human detection under fisheye cameras [27][28]. But, persons sitting or lying down or standing around the center of the omni-images do not appear like persons of posture well-detected under perspective scenes even after the omni-image is converted into a perspective or a panoramic image.…”

Section: Related Workmentioning

confidence: 99%

Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance

Nguyễn¹,

Nguyen²,

Chung³

et al. 2016

Journal of Korea Multimedia Society

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In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

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Section: Related Workmentioning

confidence: 99%

Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance

Nguyễn¹,

Nguyen²,

Chung³

et al. 2016

Journal of Korea Multimedia Society

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show abstract

“…Although the first assumption may not be necessarily true as fabrication error will always results in misalignment, it is a reasonable approximation [15,19,20,29] and will later greatly simplify the coordinate mapping in Section 4. The remapped R and h can be derived by the method of similar triangles as follows:…”

Section: Figurementioning

confidence: 99%

“…Generally, a lookup table of corresponding points will be generated so that subsequent unwrapping can be speeded up. Such practice is commonly applied in omnidirectional image unwrapping field and is documented in details in Jeng and Tsai [29] work.…”

Section: Stage 3-projection Plane For Unwrappingmentioning

confidence: 99%

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A closed form unwrapping method for a spherical omnidirectional view sensor

2013

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This article proposes a novel method of image unwrapping for spherical omnidirectional images acquired through a non-single viewpoint (NSVP) omnidirectional sensor. It has three key steps i.e. (1) calibrate the camera to obtain parameters describing the spherical omnidirectional sensor, (2) map world points onto mirror points and, subsequently, onto image points, and (3) set up the projection plane for the final image unwrapping. Based on the projection plane selected, the algorithm is able to produce three common forms of unwrapping, namely cylindrical panoramic, cuboid panoramic, and ground plane view using closed form mapping equations. The motivation for developing this technique is to address the complexity in using a NSVP omnidirectional sensor and ultimately encouraging its application in robotics field. One of the main advantages of a NSVP omnidirectional sensor is that the mirror can often be obtained at a lower price as compared to the single viewpoint counterpart.

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“…Jeng, Tsai and Wu [15,16] propose a pano-mapping table method for unwarping omni-images taken by almost any kind of omni-cameras, without requiring prior knowledge about the camera parameters in advance. In this work, a pano-mapping table is regarded as a summary of omni-camera parameters, and this pano-mapping table is created only once for a specific omni-camera.…”

Section: Introductionmentioning

confidence: 99%

Efficient omni-image unwarping using geometric symmetry

Xiong

Cheng

Basu

et al. 2010

Machine Vision and Applications

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In order to save storage space of a pano-mapping table used in omni-image unwarping, a geometric symmetry method is proposed. First of all, this method partitions a 360 • omni-image into eight 45 • omni-image sectors. Then, we partition the pano-mapping table into eight regions accordingly, with each pano-mapping table region corresponding to exactly one omni-image sector. We analyze the geometric symmetry relationship among these omni-image sectors and pano-mapping table regions. We find that if we know the mapping data in any one pano-mapping table region, it is easy to calculate the mapping data of the other seven panomapping table regions. Thus, in the final step, we perform omni-image unwarping based on only one pano-mapping table region, which reduces pano-mapping table size by seven-eighths. Reducing the pano-mapping size is very useful for implementing omni-image unwarping in embedded systems. Experiments on TI DSP-based embedded systems indicate that the proposed method reduces seven-eighths of pano-mapping table size, and improves the unwarping speed by a factor of 2.74.

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Using pano-mapping tables for unwarping of omni-images into panoramic and perspective-view images

Cited by 24 publications

References 16 publications

Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance

Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance

A closed form unwrapping method for a spherical omnidirectional view sensor

Efficient omni-image unwarping using geometric symmetry

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