Motion Estimation Algorithm Research Articles

Motion Estimation From Doppler and Spatial Data in SONAR Images

Motion estimation is critical for the localization of autonomous underwater vehicles. Current SONAR-based techniques exclusively utilize either Doppler or spatial measurements. However, these measurement domains are complementary to each other; Doppler measurements directly measure radial motion, whereas spatial measurements uniquely observe angular motion. Therefore, this article presents SONARODO (SONAR Odometry), a novel real-time motion estimation algorithm for 2-D forward-looking SONARs. It depends on a largely decoupled motion estimation process that better utilizes each measurement domain for their respective strengths. Specifically, it estimates translational motion from Doppler-azimuth images and rotational motion from range-azimuth images. While this method does require a SONAR that can provide both image types, it was designed to ensure robustness to featureless seafloor environments and low-resolution images. This article's validation with high-fidelity simulation data demonstrated that SONARODO offers accuracy and computational cost advantages over related motion estimation techniques.

Complexity reduction of test zonal search for fast motion estimation in uni-prediction of High Efficiency Video Coding

The complexity of motion estimation in the High Efficiency Video Coding (HEVC) standard is very high as it uses a number of prediction block sizes. The test zonal search (TZS) mechanism is used as motion estimation algorithm in the fast search mode of HM encoder, the reference software for HEVC. In this paper, we present schemes for reducing the complexity of the TZS algorithm for uni-prediction in HEVC. The proposed mechanisms help in reducing complexity of grid, raster and refinement search stages of TZS. The performance of the proposed mechanisms is tested independently and also combined in the fast search mode of HM-16.18. The motion estimation time and the number of search points in the combined algorithm are reduced by 68.14% and 77.10% with a BD-rate of 0.30% and a BD-PSNR of $$-0.007$$ dB in comparison with the original fast search mode in the low-delay P main profile. The proposed complexity reduction schemes are compared with those adopted in other recently proposed fast motion estimation algorithms and the performance of the proposed scheme is found to be superior in terms of the reduction in motion estimation time.

Dynamic Scene Change Detection in Video Coding

Video compression has become a source of different research studies. It is necessary in order to address channel bandwidth limitations and growing video demand, including digital libraries and streaming media delivery via the Internet. A video is a number of frames captured by a camera while a scene is a series of consecutive frames captured from a specific narrative viewpoint. To compress a video, firstly, the intra frames are separated from inter frames using scene change detection methods. Then, block-based motion estimation algorithms are used to eliminate the temporal redundancy between successive frames. This paper describes some scene change detection methods for use on the uncompressed video to detect scene types such as cut, dissolve, wipe, etc. Absolute Frame Difference (AFD), Mean Absolute Frame Differences (MAFD), Mean Histogram Absolute Frame Difference (MHAFD), and Maximum Gradient Value (MGV) techniques are adaptively tested on different video types to identify accurate scene change in both low and high object motion scenes. Test results show that the proposed approach (MHAFD) obtains a better accuracy, F1-scor measure of 100%, especially for cuts and gradual transitions (wipe) video types. Dissolve scene change is detected with a high precision of 100% (i.e., no false detection) with the (MAFD) detector. Besides, in terms of time complexity for analyzing all the video samples, the proposed method (MHAFD ) provides the best result compared to the selected detectors.

Block Based Motion Estimation Algorithms: Analysis

several video coding standards and techniques have been introduced for multimedia applications, particularly h.26x series for video processing. These standards employ motion estimation process in order to reduce the amount of data that is required to store or transmit the video. Motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search algorithm, many fast search and fast full search block based algorithms has been presented. An evaluation of up to date motion estimation algorithms, based on a number of empirical results on several test video sequences, is presented as well.

Towards efficient motion-blurred public security video super-resolution based on back-projection networks

In public security, video super-resolution has been an important technology for assisting users in effectively locating potential safety hazards. However, the video collected from video monitoring equipment is usually polluted by motion blur and noise, which results in low video quality after reconstruction. In this paper, we propose a super-resolution reconstruction model for motion-blurred video to reconstruct corresponding high-resolution video from motion-blurred low-resolution video. First, a single-frame image super-resolution model is designed based on the improved deep back-projection network, which primarily includes the design of a recursive method. Second, a multi-frame image super-resolution model is designed based on an optical motion algorithm for motion estimation and motion compensation of video frame sequences. Finally, according to the motion-blurred low-resolution video pollution condition, the de-blurring algorithm and multi-frame image super-resolution are combined to propose a super-resolution reconstruction model for motion-blurred video. Experiments show that the proposed motion-blurred video super-resolution model is superior to other contrasted algorithms in clear video and motion-blurred video reconstruction and has a better reconstruction effect and higher practical value.

Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging

Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p < 0.001). Contrast-to-noise (CNRe) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNRe of −0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p < 0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

VLSI Architecture of Block Matching Algorithms for Motion Estimation in High Efficiency Video Coding

High-efficiency video coding (HEVC) is a latest video coding standard and the motion estimation unit is the most important block. The work presents the different types of Matching Criteria for Block-Based Motion Estimation technique in HEVC standard. HEVC requires fast motion estimation algorithms to have better real time performance. The hardware implementation of motion estimation helps to achieve high speed though parallel processing. An improved block matching technique is designed with reduced blocks for HEVC. The proposed method has less execution time where only blocks having motion are compared for prediction computation. The searching time complexity is dependent on the number of blocks that are having motion. The searching time of frame having small motion can be reduced to 80–85% as compared to the traditional full search algorithm. In the paper, sum of absolute difference, mean square error and mean absolute difference are computed to find the best matching algorithm for HEVC. However, SAD has less computational complexity with compare to other matching criteria. The results suggest that proposed motion estimation algorithm has better performance with compare to similar previous work.

A low complexity and computationally scalable fast motion estimation algorithm for HEVC

Motion Estimation (ME) is one of the most computationally demanding parts of video encoders. The Test Zone (TZ) search is a popular fast ME algorithm, which is recommended for High-Efficiency Video Coding (HEVC). While the TZ search achieves an excellent coding efficiency, it is not a favorable choice for hardware implementations due to 1) a relatively high computational complexity, 2) inducing data dependency among the neighboring blocks, which complicates hardware implementations and parallel processing in software implementations, and 3) lack of computational adjustability, which is required for video encoding in power-constrained devices. This paper diagnoses the cause of these issues to be in the multiple starting search points of the TZ search algorithm. Accordingly, a method is proposed to find a single reliable starting point that replaces the first step of the TZ search algorithm. To do so, both current and reference frames are analyzed using a complex wavelet transform, and similar salient points are identified among the two frames. Then a light-weight process is used to match these points to find a single reliable starting point. The reliability of this point leads to reduced zonal refinement range with negligible cost in compression efficiency. Since adjusting the refinement range can be used as an effective way for adjusting the complexity, this results in a computationally scalable ME algorithm, named FMECWT. In contrast to the existing methods, FMECWT does not rely on neighboring blocks, which eliminates the inherent data dependency of TZ search. Experimental results show that FMECWT achieves ~35% to ~85% ME time reduction compared to TZ search, with only 0.1% to 1.7% increase in BD-Rate.

Improved motion estimation algorithm based on integrity index and its implementation in x265

With the development of fast motion estimation (ME) algorithms towards video compression standard H.264, burden on integer-pel ME is slightly reduced. However the integer-pel ME is becoming computationally complex due to demand of higher and higher resolution videos. On the other hand, there is always trade-off between the speed and performance of these search algorithms. This work improvises the performance of content awareness enabled integer-pel ME algorithm for encoding the fast and slow motion video sequences of high definition (HD) resolution category viz. 1280×720 and 1920×1080. The algorithm proposes a novel notion of 'integrity index' with focus on increasing the PSNR and reducing the bit rate. It is implemented in the frame work of x265 version 1.7 video encoder. Motion independent ME algorithm is analysed quantitatively in terms of parameters based on rate distortion and ME time. The proposed algorithm is found to be performing better on BD-Rate and BD-PSNR parameters for the videos of both resolutions under consideration. Observed increase in motion estimation time is less than four seconds compared to hexagonal search algorithm of ME which is the benchmark in fast ME algorithm.

Super-Resolution with compressively sensed MR/PET signals at its input

Abstract The aim of this paper is to present a highly effective Magnetic Resonance Imaging- Positron Emission Tomography (MR/PET) image reconstruction strategy allowing for simultaneous resolution enhancing and scanning time minimisation. The presented algorithm employs the combined sparsity, compressed sensing (CS) theory and super resolution to achieve high-resolution output maintaining data collecting steps at the lowest possible levels. This paper presents a very promising application of super-resolution of highly sensitively compressed MR/PET raw data. The presented algorithm nests image priors, deblurring, and a discrete dense displacement sampling for the deformable registration of high-resolution images at its core. Data from preliminary trials can also be valuable in providing background information useful in reducing examinations times. In accordance with expectations, the presented algorithm can enhance image resolution without any hardware modifications. However, the motion estimation algorithm can drastically eliminate diagnostic image artifacts that increase the chances of a correct diagnosis. The robustness of the suggested algorithm was subjected to state-of-the art image resolution enhancement algorithms: 3D kernel regression, Enhanced Deep Residual Networks for Single Image Super-Resolution, Image Super-Resolution Using Very Deep Residual Channel Attention Networks, Residual Dense Network for Image Super-Resolution. It is worth underlining that combining Compressed Sensing with its conjugate symmetry, as well as Partial Fourier methodology leads to data acquisition acceleration when compared to the different and unmodified k-space sampling patterns. It can be clearly seen that the obtained improvements have led to much better sharpness, edge interpretations, and contrast. Moreover, the accomplishments have been validated by PSNR. In accordance with expectations, the presented algorithm is able to enhance image resolution without any hardware adjustments. Besides the resolution tradeoffs, this method is able to minimise motion artifacts what is especially important for effective physician-to-physician communication and unbiased diagnosis.

High-Performance Distributed Compressive Video Sensing: Jointly Exploiting the HEVC Motion Estimation and the ℓ1 – ℓ1 Reconstruction

The distributed compressive video sensing (DCVS) system combines the advantages of compressed sensing (CS) and distributed video coding (DVC), suitable for the limited-resource video sensing and transmission environment. In this paper, we propose a comprehensive high performance DCVS system. First, we introduce the BM3D-AMP algorithm reconstruct key (K) frames. Second, we propose a new high efficiency video coding (HEVC) motion estimation (ME) algorithm with motion vector (MV) prediction method. By integrating the segmentation idea and motion estimation, this algorithm can gets more accurate side information (SI). Finally, we propose the l 1 - l 1 minimization model to achieve non-key (NK) frames joint high-quality reconstruction. We utilize the alternating direction method of multipliers (ADMM) algorithm to solve it. With the idea of dividing and conquering, the general problem is decomposed into several smaller pieces. Experimental results demonstrate that the proposed system has significant improvement over its counterparts.

Camera motion estimation algorithm for IoT devices based on optimized feature tracking method

Abstract This paper presents an optimized feature tracking mechanism. The proposed mechanism outperforms our previously proposed algorithms which are on the basis of feature matching. Specifically, for the work process of the proposal, extracting the FAST feature from input images at first. Then, by applying optical flow algorithm, the feature points appeared in different images will be tracked and marked out in the images. Afterwards, we apply the optimized PnP algorithm to estimate the camera motion. The results of the evaluation experiments which are conducted on raspberry pi indicate that our proposed mechanism provides a 16.7% improvement in time-efficiency compared with our previous work. Furthermore, the proposed mechanism still keeps the estimation error at a lowest level, as our previous work did. In summary, the proposed mechanism achieves higher time-efficiency and good estimation accuracy on IoT devices, which only have constrained sources.

Improved motion estimation algorithm based on integrity index and its implementation in x265

Improved motion estimation algorithm based on integrity index and its implementation in x265

Drift-Free and Self-Aligned IMU-based Human Gait Tracking System with Augmented Precision and Robustness

IMU-based human joint motion acquisition system is attractive for real-time control and monitoring in the emerging wearable technology due to its portability. However, in practical applications, it heavily suffers from long-term drift, magnetic interference and inconsistency of rotational reference frames, which causes precision degradation. In this letter, a novel on-line IMU-based human gait estimation framework was proposed to obtain the joint rotational angles directly under the kinematic constraints between multiple body segments, whereas traditional methods need to estimate the orientation of each individual segment. This framework consists of an on-line algorithm to align IMU frames with human joints and motion estimation algorithms for hip and knee without the aid of magnetometer. Both a 2-DoF robot and human gait tests were performed to validate the proposed method as compared with the predictions from commercial IMUs, joint encoders and an optical tracking system. The outcome demonstrated its advantages of adaptive alignment, drift rejection and low computational cost, which alleviates the practical barriers faced by human motion data collection in the wearable devices.

Low Power Oriented Full Search Block Based Motion Estimation (LP-FSBME) Architecture Using Power Efficient Adder Compressor For H.265 Coding Techniques.

H.265 coding is known as HIGH efficiency video coding (HEVC). This is most successful video compression standard and extended from H.264/MPEG-4 advanced video coding (AVC) for same level of video quality. However, H.265 improved better video quality for same bit rate. In video coding, motion estimation (ME) is determined the motion vector from adjacent frames. Various algorithms have been introduced by many researchers to accomplish low power oriented ME. However, low power oriented full search block based motion estimation (LP-FSBME) algorithm gives accurate results. Architecture of sum of absolute difference (SAD) is used an adder tree to accumulate the processing elements. Power efficient 16:2 adder compressors in SAD architecture reduce the power dissipation rather than convention adders in SAD architecture. The hardware implementation of proposed method is done in Xilinx Virtex 7 FPGA XC7VX1140T device with speed grade 1 in Xilinx software version 14.5 tool, developed in Verilog Hardware Description Language (Verilog-HDL), and simulated in ISE simulator for tennis, BQ terrace and Kimono videos with the resolution of 1080x720 pixels with 30fps.

A Multi-Grained Parallel Solution for HEVC Encoding on Heterogeneous Platforms

To improve the parallel processing capability of video coding, the emerging high efficiency video coding (HEVC) standard introduces two parallel techniques, i.e., Wavefront Parallel Processing (WPP) and Tiles , to make it much more parallel-friendly than its predecessors. However, these two techniques are designed to explore coarse-grained parallelism in HEVC encoding on multicore Central Processing Unit (CPU) platforms. As the computing architecture undergoes a trend toward heterogeneity in the last decade, multi-grained parallel computing methods can be designed to accelerate HEVC encoding on heterogeneous systems. In this paper, a multi-grained parallel solution (MPS) is proposed to optimize HEVC encoding on a typical heterogeneous platform. A massively parallel motion estimation algorithm is employed by MPS to parallelize part of HEVC encoding on Graphic Processing Unit (GPU). Meanwhile, several other HEVC encoding modules are accelerated on CPU through the cooperation of WPP and an adaptive parallel mode decision algorithm. The parallelism between CPU and GPU is well designed and implemented to guarantee an efficient concurrent execution of HEVC encoding on multi-grained parallel levels. The effectiveness of the proposed MPS for HEVC encoding is verified on a number of experiments.

How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field

Nowadays, as the amount of video content increases, the importance of global motion estimation is increasing more and more. This paper considers motion estimation within a compressed domain and presents a robust non-iterative global motion estimation algorithm that efficiently eliminates outliers. The said algorithm is comprised of two parts. The first part uses the distinct-outlier mask based on statistical analysis to eliminate the distinct outliers and uses the current-object mask based on the reference-object mask to eliminate the object outliers. In the second part, the proposed median error designed to be robust to the outlier ratio has been used to estimate weights with higher values as the motion vector gets closer to global motion. Using these weights, the proposed algorithm can accurately estimate global motion non-iteratively, although the outlier ratio may change. The simulation results demonstrate that the proposed algorithm demonstrates the highest estimation accuracy and processing speed in synthetic motion fields as well as real video sequences.

Fast global motion estimation on single instruction multiple data processors for real-time devices

Global motion estimation is a fundamental tool for image processing and computer vision. Since low-cost real-time devices have limited computational resources, the computational burden of global motion estimation remains considerable for low-cost devices. Hence, a fast global motion estimation algorithm for single instruction multiple data (SIMD) processors with which a CPU in modern mobile computing devices is commonly equipped is proposed. Conventional projection-based global motion estimation is modified to improve the prediction accuracy for videos containing large motions. To significantly increase the parallelism, the exact sizes of the variables in each processing step of the proposed algorithm are determined by considering the size of an SIMD register. Simulation results demonstrate that the proposed scheme is considerably fast and accurate, even for videos containing large motions.

Method for joint flexion angle estimation using UWB ranging with clock model compensation

This paper presents a wearable system for measurement and monitoring human body joint angles based on UWB ranging. The DW1000 chip was used with standard deviation of distance measurement within 10 cm with range up to 70 m. We propose a method for enhancing range measurement accuracy based on an estimator which compensates clock imperfections and relative pairwise movement of nodes. Since the estimator is valid only for small slices of time, we propose continuous motion estimation algorithm based on segment-by-segment data processing and stitching results into a final solution. The pairwise distances are approximated with Taylor series of a given order L in short measurement windows while timestamps are compensated with clock parameters of a first-order clock model. The main contribution of the proposed method is the ability to implement joint angle estimation by using low-cost off-the-shelf UWB components, without high-precision clock sources or a need for wired or wireless time synchronization. In order to determine an optimum order L and time slice length, Sprague and Geers' metric was used. The method was experimentally evaluated in static and dynamic conditions. The results show that the accuracy of the proposed system is comparable to similar solutions based on laboratory equipment.

Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations

As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth's surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy.