An image mosaicing module for wide-area surveillance

Heikkilä, Marko

doi:10.1145/1099396.1099400

Cited by 21 publications

(13 citation statements)

References 20 publications

(23 reference statements)

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“…Heikkila et al [11] also propose an automatic image mosaicing method for wide area surveillance. Their method extracts SIFT features from the incoming images and then constructs a mosaic.…”

Section: Related Workmentioning

confidence: 99%

“…One is based upon feature similarity, in other words matches that measure similarity between feature points expressed in a feature space. For example, Heikkila et al [11] used SIFT features to describe SIFT points and used the euclidean distance in the SIFT feature space to map the SIFT points. The other is to use a tracker to move feature points forward from frame t − 1 to frame t. Mei et al [18] use the Kanade-Lucas-Tomasi feature tracker (KLT) [25].…”

Section: Stabilization Algorithmsmentioning

confidence: 99%

“…The two baseline algorithms are roughly modeled after the work by Heikkila et al [11] and Mei et al [18]. The new algorithm introduced here uses a two-stage process to refresh a set of SIFT feature points that are tracked using the KLT algorithm.…”

Section: Baseline Algorithmsmentioning

confidence: 99%

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Video alignment to a common reference

Dutta

Draper

Beveridge

2014

IEEE Winter Conference on Applications of Computer Vision

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Video Alignment to a Common ReferenceHandheld videos often include unintentional motion (jitter) and intentional motion (pan and/or zoom). Human viewers prefer to see jitter removed, creating a smoothly moving camera. For video analysis, in contrast, aligning to a fixed stable background is sometimes preferable. This paper presents an algorithm that removes both forms of motion using a novel and efficient way of tracking background points while ignoring moving foreground points.The approach is related to image mosaicing, but the result is a video rather than an enlarged still image. It is also related to multiple object tracking approaches, but simpler since moving objects need not be explicitly tracked. The algorithm presented takes as input a video and returns one or several stabilized videos. Videos are broken into parts when the algorithm detects background change and it becomes necessary to fix upon a new background. We present two techniques in this thesis. One technique stabilizes the video with respect to the first available frame. Another technique stabilizes the videos with respect to a best frame. Our approach assumes the person holding the camera is standing in one place and that objects in motion do not dominate the image. Our algorithm performs better than previously published approaches when compared on 1,401 handheld videos from the recently released Point-and-Shoot Face Recognition Challenge (PASC).ii

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“…Heikkila et al [11] also propose an automatic image mosaicing method for wide area surveillance. Their method extracts SIFT features from the incoming images and then constructs a mosaic.…”

Section: Related Workmentioning

confidence: 99%

Section: Stabilization Algorithmsmentioning

confidence: 99%

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Video alignment to a common reference

Dutta

Draper

Beveridge

2014

IEEE Winter Conference on Applications of Computer Vision

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“…In our method blending is done with a gaussian weighting mask similarly to [13] if no moving objects are present. If there are moving objects, the seam is drawn outside the boundaries of moving objects.…”

Section: Stitching Phasementioning

confidence: 99%

Panoramas from Partially Blurred Video

Boutellier

Silvén

2006

Advances in Machine Vision, Image Processing, and Pattern Analysis

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Abstract. Numerous high-quality image stitching algorithms have been published in the recent years. Mosaics created by these methods are of high quality if the input images are not distorted. However, if the source images are blurred, parts of the resulting mosaic will be blurred also. In this paper we propose a method to create high-quality panoramas from video sequences that contain also low-quality frames. Moreover, our method is computationally efficient, which makes it attractive for hand-held devices. The algorithm uses motion detection to display correctly moving objects in the sequence. The colors of the mosaic are also balanced to handle changes in camera exposure times.

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“…Many automatic digital mosaicing (stitching, panorama) methods have been developed [1,2,3,4,5], but unfortunately their evaluation has been only qualitative. There seems to exist some generally used image sets for mosaicing, for instance the "S. Zeno" (e.g.…”

Section: Introductionmentioning

confidence: 99%

Image Based Quantitative Mosaic Evaluation with Artificial Video

2009

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Abstract. Interest towards image mosaicing has existed since the dawn of photography. Many automatic digital mosaicing methods have been developed, but unfortunately their evaluation has been only qualitative. Lack of generally approved measures and standard test data sets impedes comparison of the works by different research groups. For scientific evaluation, mosaic quality should be quantitatively measured, and standard protocols established. In this paper the authors propose a method for creating artificial video images with virtual camera parameters and properties for testing mosaicing performance. Important evaluation issues are addressed, especially mosaic coverage. The authors present a measuring method for evaluating mosaicing performance of different algorithms, and showcase it with the root-mean-squared error. Three artificial test videos are presented, ran through real-time mosaicing method as an example, and published in the Web to facilitate future performance comparisons.

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An image mosaicing module for wide-area surveillance

Cited by 21 publications

References 20 publications

Video alignment to a common reference

Video alignment to a common reference

Panoramas from Partially Blurred Video

Image Based Quantitative Mosaic Evaluation with Artificial Video

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