An Image-Sequence Compressing Algorithm Based on Homography Transformation for Unmanned Aerial Vehicle

Gong, Junbin; Zheng, Chenlin; Tian, Jinwen; Dingxue, Wu

doi:10.1109/iptc.2010.178

Cited by 17 publications

(8 citation statements)

References 5 publications

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“…Computation is reduced by using prior knowledge on camera locations (from available mini-UAV sensor data) and projective geometry of the camera. Based on the algorithm in [35], Gong et al [36] proposed a low-complexity image-sequence compressing algorithm for UAVs. Bhaskaranand et al [37] designed a video encoding scheme suitable for applications in which encoder complexity needs to be low (e.g., UAV video surveillance).…”

Section: Related Workmentioning

confidence: 99%

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Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Li²,

Ding

et al. 2015

Remote Sensing

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Global motion estimation (GME) is a key technology in unmanned aerial vehicle remote sensing (UAVRS). However, when a UAV's motion and behavior change significantly or the image information is not rich, traditional image-based methods for GME often perform poorly. Introducing bottom metadata can improve precision in a large-scale motion condition and reduce the dependence on unreliable image information. GME is divided into coarse and residual GME through coordinate transformation and based on the study hypotheses. In coarse GME, an auxiliary image is built to convert image matching from a wide baseline condition to a narrow baseline one. In residual GME, a novel information and contrast feature detection algorithm is proposed for big-block matching to maximize the use of reliable image information and ensure that the contents of interest are well estimated. Additionally, an image motion monitor is designed to select the appropriate processing strategy by monitoring the motion scales of translation, rotation, and zoom. A medium-altitude UAV is employed to collect three types of large-scale motion datasets. Peak signal to noise ratio (PSNR) and motion scale are computed. This study's result is encouraging and applicable to other medium-or high-altitude UAVs with a similar system structure.

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Section: Related Workmentioning

confidence: 99%

“…Thus, all pixels of an image are assumed to be on the same plane. Field depth consistency is a basic hypothesis in most studies on GME [35][36][37][38][39]. The lower the terrain fluctuations and buildings are, the more accurate the result of GME is.…”

Section: Study Hypothesesmentioning

confidence: 99%

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Li²,

Ding

et al. 2015

Remote Sensing

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“…This 3D model is referred to as the image homography [7]. The images can then be warped and positioned with respect to the 3D model [8,9]. This process involves modifying the image matrices to generate the stitched image.…”

Section: Introductionmentioning

confidence: 99%

Real Time Video Stitching by Exploring Temporal and Spatial Features

Blair

et al. 2017

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

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Although image stitching has been investigated for years, realtime video stitching still lacks of efficient methods to meet the required frame rate for satisfactory human vision experience. This work proposes efficient video stitching solutions by exploiting both temporal and spatial features among video frames. As a result, the stitching speed is significantly improved with two techniques by exploiting:(1) the dimmension of distance (spatial) by focusing only on the region of frame overlap and (2) the dimmension of time (temporal) by reusing homography information across multiple frames. Based on these two techniques, this paper presents three solutions to determine submiages for rapid stitching the video frames from side-by-side cameras. This work implements these solutions into a video stitcher. The evaluation over video streams shows that the proposed solutions can stitch the video at 6.5 frames per second (fps) in contrast to 1.5 fps in conventional imaging stitching approaches, which is over 400% improvement on stitching speed performance, but at the cost of a marginal drop in accuracy.

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“…In (Morimoto et al, 1997) and (Steinbach et al, 1999) GM parameters are used to compensate frames that are used as reference for block ME using GM within standard MPEG-4 and H.264 codecs. In (Gong et al, 2010) authors propose a framework tailored for UAV applications that uses the GM information and an homography model to code the stream using JPEG2000. In (Soares and Pinho, 2013) and (Angelino et al, 2013b) authors present modifications of the H.264/AVC encoder to initialize the motion vectors (MVs) using the camera motion information from UAV sensors.…”

Section: Introductionmentioning

confidence: 99%

A Sensor Aided H.264/AVC Video Encoder for Aerial Video Sequences With in the Loop Metadata Correction

Cicala

Angelino

Ruatta

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on board and then processed with on-ground desktop software. In such a way the computational load, and hence the power consumption, is moved on ground, leaving on board only the task of storing data. Such an approach is important in the case of small multi-rotorcraft UAVs because of their low endurance due to the short battery life. Images can be stored on board with either still image or video data compression. Still image system are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long and when the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate global motion parameters without video analysis. A low complexity image analysis can be still performed in order to refine the motion field estimated using only the metadata. In this work, we propose to use this refinement step in order to improve the position and attitude estimation produced by the navigation system in order to maximize the encoder performance. Experiments are performed on both simulated and real world video sequences.

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An Image-Sequence Compressing Algorithm Based on Homography Transformation for Unmanned Aerial Vehicle

Cited by 17 publications

References 5 publications

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

Real Time Video Stitching by Exploring Temporal and Spatial Features

A Sensor Aided H.264/AVC Video Encoder for Aerial Video Sequences With in the Loop Metadata Correction

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