On active camera control and camera motion recovery with foveate wavelet transform

Wei, Jie; Li, Z.-N.

doi:10.1109/34.946992

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Therefore, the ROI output must be processed for optimal control of the virtual camera. Examples include the 3D virtual cinematographer by He et al [13], 3D animation by Gleicher and Witkin [11], and fovea area view by Wei and Li [32]. Since the work here involves moving a small ROI rectangle inside a large panoramic video, our virtual camera control is equivalent to controlling a moving camera in a 2D image plane.…”

Section: Related Workmentioning

confidence: 99%

Region of interest extraction and virtual camera control based on panoramic video capturing

Sun

Foote

Kimber

et al. 2005

IEEE Trans. Multimedia

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Abstract-We present a system for automatically extracting the region of interest and controlling virtual cameras control based on panoramic video. It targets applications such as classroom lectures and video conferencing. For capturing panoramic video, we use the FlyCam system that produces high resolution, wide-angle video by stitching video images from multiple stationary cameras. To generate conventional video, a region of interest (ROI) can be cropped from the panoramic video. We propose methods for ROI detection, tracking, and virtual camera control that work in both the uncompressed and compressed domains. The ROI is located from motion and color information in the uncompressed domain and macroblock information in the compressed domain, and tracked using a Kalman filter. This results in virtual camera control that simulates human controlled video recording. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing.

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Section: Related Workmentioning

confidence: 99%

Region of interest extraction and virtual camera control based on panoramic video capturing

Sun

Foote

Kimber

et al. 2005

IEEE Trans. Multimedia

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“…One led by Tordoff and Murray (2004) utilizes SFM to recover the target's 3D depth, and the other proposed by Fayman et al (2001) is based on the optical flow of the image sequence. In addition to pure spatial domain processing, the wavelet transform is another promising approach addressed by Wei and Li (2001). Among existing size preserving tracking algorithms, the SFM based algorithm appears to be the most promising primarily due to its superior accuracy and moderate computational complexity.…”

Section: Introductionmentioning

confidence: 99%

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

Yao

Abidi

2008

Int J Comput Vis

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To achieve size preserving tracking, in addition to controlling the camera's pan and tilt motions to keep the object of interest in the camera's field of view (FOV), the camera's focal length is adjusted automatically to compensate for the changes in the target's image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset.Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target's 3D geometry and motion and is able to accommodate a large amount of offplane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algo-Y. Yao · B. Abidi ( ) · M. Abidi Imaging, Robotics, and rithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.

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“…Although there have been many fruitful results along this line of research, it is very difficult to perform high level vision tasks without active participation from the vision system. It is precisely for this reason that active vision is proposed [3,4] for which efforts are made for computer controlled cameras to actively participate in the visual perception process, much similar to the body and eye movements of human vision systems [5]. Of course, when the camera is operated by a human being as in the case of movie making and even home video making, the lines of reasoning advocated in active vision research can essentially be reversed to form a bridge to connect the conceptual gap between the visual world and the underlying semantic meanings.…”

Section: Introductionmentioning

confidence: 99%

Automatic object extraction and reconstruction in active video

Lü

2008

Pattern Recognition

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A new method of video object extraction is proposed to automatically extract the object of interest from actively acquired videos. Traditional video object extraction techniques often operate under the assumption of homogeneous object motion and extract various parts of the video that are motion consistent as objects. In contrast, the proposed active video object extraction (AVOE) approach assumes that the object of interest is being actively tracked by a non-calibrated camera under general motion and classifies the possible movements of the camera that result in the 2D motion patterns as recovered from the image sequence. Consequently, the AVOE method is able to extract the single object of interest from the active video. We formalize the AVOE process using notions from Gestalt psychology. We define a new Gestalt factor called "shift and hold" and present 2D object extraction algorithms. Moreover, since an active video sequence naturally contains multiple views of the object of interest, we demonstrate that these views can be combined to form a single 3D object regardless of whether the object is static or moving in the video.

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On active camera control and camera motion recovery with foveate wavelet transform

Cited by 10 publications

References 23 publications

Region of interest extraction and virtual camera control based on panoramic video capturing

Region of interest extraction and virtual camera control based on panoramic video capturing

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

Automatic object extraction and reconstruction in active video

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