Mr. Hongwei Mao Profile

Hongwei Mao

at Arizona State Univ

SPIE Involvement:

Author

Area of Expertise:

Image and video processing , Artificial neural networks , Cognitive neuroscience , Video target detection and tracking , Brain-machine interface

Publications (7)

SPIE Journal Paper | 6 January 2014

Automatic detection and tracking of multiple interacting targets from a moving platform

Hongwei Mao, Chenhui Yang, Glen Abousleman, Jennie Si

OE, Vol. 53, Issue 01, 013102, (January 2014) https://doi.org/10.1117/12.10.1117/1.OE.53.1.013102

KEYWORDS: Target detection, Automatic tracking, Video, Cameras, Optical tracking, Target recognition, Detection and tracking algorithms, Optical engineering, Video surveillance, Binary data

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Proceedings Article | 31 May 2013 Paper

An integrated multitarget tracking system for interacting target scenarios

Hongwei Mao, Glen Abousleman, Jennie Si

Proceedings Volume 8713, 871314 (2013) https://doi.org/10.1117/12.2017661

KEYWORDS: Target detection, Video, Video surveillance, Cameras, Detection and tracking algorithms, System integration, Target recognition, Automatic tracking, Blob detection, Image segmentation

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Proceedings Article | 31 May 2013 Paper

Efficient parallel implementation of real-time airborne target tracking system on heterogeneous multicore SoC

Xiang Gao, Hongwei Mao, Eric Munson, Glen Abousleman, Jennie Si

Proceedings Volume 8713, 871316 (2013) https://doi.org/10.1117/12.2016024

KEYWORDS: Digital signal processing, Target detection, Detection and tracking algorithms, Video, Signal processing, System on a chip, Motion estimation, Cameras, Video processing, Optical flow

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Proceedings Article | 26 May 2011 Paper

Tracking targets through occlusions in outdoor videos

Hongwei Mao, Chenhui Yang, Glen Abousleman, Jennie Si

Proceedings Volume 8020, 80200P (2011) https://doi.org/10.1117/12.883704

KEYWORDS: Target detection, Video, Filtering (signal processing), Motion detection, Video surveillance, Electronic filtering, Detection and tracking algorithms, Buildings, Motion models, Video processing

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Proceedings Article | 26 May 2011 Paper

Software-based robust global motion estimation for real-time video target tracking

Chenhui Yang, Hongwei Mao, Glen Abousleman, Jennie Si

Proceedings Volume 8020, 80200O (2011) https://doi.org/10.1117/12.883634

KEYWORDS: Video, Motion estimation, Cameras, Optical flow, Lawrencium, Surveillance, Reconnaissance, Intelligence systems, Reconnaissance systems, Current controlled current source

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Proceedings Article | 26 April 2010 Paper

Automated multiple target detection and tracking in UAV videos

Hongwei Mao, Chenhui Yang, Glen Abousleman, Jennie Si

Proceedings Volume 7668, 76680J (2010) https://doi.org/10.1117/12.849739

KEYWORDS: Target detection, Video, Unmanned aerial vehicles, Binary data, Cameras, Image segmentation, Motion estimation, Motion models, Detection and tracking algorithms, Filtering (signal processing)

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Proceedings Article | 26 April 2010 Paper

Correction of projective distortion in long-image-sequence mosaics without prior information

Chenhui Yang, Hongwei Mao, Glen Abousleman, Jennie Si

Proceedings Volume 7668, 76680Q (2010) https://doi.org/10.1117/12.849854

KEYWORDS: Distortion, Video, Affine motion model, Image processing, Unmanned aerial vehicles, Video surveillance, Cameras, Video processing, Surveillance, Imaging systems

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Image mosaicking is the process of piecing together multiple video frames or still images from a moving camera to form a wide-area or panoramic view of the scene being imaged. Mosaics have widespread applications in many areas such as security surveillance, remote sensing, geographical exploration, agricultural field surveillance, virtual reality, digital video, and medical image analysis, among others. When mosaicking a large number of still images or video frames, the quality of the resulting mosaic is compromised by projective distortion. That is, during the mosaicking process, the image frames that are transformed and pasted to the mosaic become significantly scaled down and appear out of proportion with respect to the mosaic. As more frames continue to be transformed, important target information in the frames can be lost since the transformed frames become too small, which eventually leads to the inability to continue further. Some projective distortion correction techniques make use of prior information such as GPS information embedded within the image, or camera internal and external parameters. Alternatively, this paper proposes a new algorithm to reduce the projective distortion without using any prior information whatsoever. Based on the analysis of the projective distortion, we approximate the projective matrix that describes the transformation between image frames using an affine model. Using singular value decomposition, we can deduce the affine model scaling factor that is usually very close to 1. By resetting the image scale of the affine model to 1, the transformed image size remains unchanged. Even though the proposed correction introduces some error in the image matching, this error is typically acceptable and more importantly, the final mosaic preserves the original image size after transformation. We demonstrate the effectiveness of this new correction algorithm on two real-world unmanned air vehicle (UAV) sequences. The proposed method is shown to be effective and suitable for real-time implementation.

Showing 5 of 7 publications

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