Optical Flow-based Motion Estimation Research Articles

Motion Vector Extrapolation for Video Object Detection.

Despite the continued successes of computationally efficient deep neural network architectures for video object detection, performance continually arrives at the great trilemma of speed versus accuracy versus computational resources (pick two). Current attempts to exploit temporal information in video data to overcome this trilemma are bottlenecked by the state of the art in object detection models. This work presents motion vector extrapolation (MOVEX), a technique which performs video object detection through the use of off-the-shelf object detectors alongside existing optical flow-based motion estimation techniques in parallel. This work demonstrates that this approach significantly reduces the baseline latency of any given object detector without sacrificing accuracy performance. Further latency reductions up to 24 times lower than the original latency can be achieved with minimal accuracy loss. MOVEX enables low-latency video object detection on common CPU-based systems, thus allowing for high-performance video object detection beyond the domain of GPU computing.

光流法运动估计在FPGA上的实现与性能分析

光流法运动估计在FPGA上的实现与性能分析

Papers] Automatic Self-Improvement Scheme in Optical Flow-Based Motion Estimation for Sequential Fisheye Images

Papers] Automatic Self-Improvement Scheme in Optical Flow-Based Motion Estimation for Sequential Fisheye Images

6-DOF motion estimation using optical flow based on dual cameras

Because of its characteristics of simple algorithm and hardware, optical flow-based motion estimation has become a hot research field, especially in GPS-denied environment. Optical flow could be used to obtain the aircraft motion information, but the six-(degree of freedom) (6-DOF) motion still couldn’t be accurately estimated by existing methods. The purpose of this work is to provide a motion estimation method based on optical flow from forward and down looking cameras, which doesn’t rely on the assumption of level flight. First, the distribution and decoupling method of optical flow from forward camera are utilized to get attitude. Then, the resulted angular velocities are utilized to obtain the translational optical flow of the down camera, which can eliminate the influence of rotational motion on velocity estimation. Besides, the translational motion estimation equation is simplified by establishing the relation between the depths of feature points and the aircraft altitude. Finally, simulation results show that the method presented is accurate and robust.

Statistical analysis of three‐dimensional optical flow separability in volumetric images

Three-dimensional (3D) motion analysis of dynamic body organs using volumetric images is of increasing interest in different computer vision applications. A number of methods for estimation of 3D optical flow in those images have been developed in recent years. However, theoretical limits of 3D optical flow-based motion estimation and segmentation are yet to be analysed. In this study, a statistical analysis of 3D optical flow is presented and the results are used to predict the separability of local 3D motions. Experimental results, using both synthetic and real images, demonstrate the applicability of the proposed analysis to predict the separability of two motions in terms of the parameters quantifying their relative motion and the scale of measurement noise.

Video human segmentation based on multiple-cue integration

A multiple-cue integration based video human segmentation model is proposed in this paper. The proposed model firstly describes the local color distribution in an image via training competing 1SVMs and computes shape priors of video human objects by optical flow-based motion estimation. And then the local color model and the shape priors are integrated into the image segmentation framework. Additionally, the weights of different energy terms in the segmentation model are adaptively adjusted according to relevant feedback cues. The experiments have indicated that the proposed model can describe well the foreground (for example, human object) change from video in complex environments. Besides improving the segmentation accuracy for the current frame, the proposed model improves the automatic segmentation accuracy for the subsequent frames because of significantly reducing the adverse impacts on the segmentation for the subsequent frames caused by segmentation error of the current frame.