Motion-Based Template Matching for Obstacle Detection

2004 ◽  
Vol 8 (5) ◽  
pp. 469-476
Author(s):  
Kazuhiko Kawamoto ◽  
◽  
Naoya Ohnishi ◽  
Atsushi Imiya ◽  
Reinhard Klette ◽  
...  
Keyword(s):  
Optical Flow ◽  
Template Matching ◽  
Roc Curves ◽  
Image Sequences ◽  
Obstacle Detection ◽  
Frame Rate ◽  
Video Frame ◽  
Positive Fraction ◽  
Indoor Scenes ◽  
The Difference

A matching algorithm that evaluates the difference between model and calculated flows for obstacle detection in video sequences is presented. A stabilization method for obstacle detection by median filtering to overcome instability in the computation of optical flow is also presented. Since optical flow is a scene-independent measurement, the proposed algorithm can be applied to various situations, whereas most of existing color- and texture-based algorithms depend on specific scenes, such as roadway and indoor scenes. An experiment is conducted with three real image sequences, in which a static box or a moving toy car appears, to evaluate the performance in terms of accuracy under varying thresholds using a receiver operating characteristic (ROC) curve. For the three image sequences, the ROC curves show, in the best case, that the false positive fraction and the true positive fraction is 19.0% and 79.6%, 11.4% and 84.5%, 19.0% and 85.4%, respectively. The processing time per frame is 19.38msec. on 2.0GHz Pentium 4, which is less than the video-frame rate.

Mouth Shape Detection Based on Template Matching and Optical Flow for Machine Lip Reading

2013 ◽  
Vol 1 (1) ◽  
pp. 14-25 ◽  
Author(s):  
Tsuyoshi Miyazaki ◽  
Toyoshiro Nakashima ◽  
Naohiro Ishii
Keyword(s):  
Optical Flow ◽  
Template Matching ◽  
High Speed ◽  
Image Sequences ◽  
Previous Method ◽  
The Other ◽  
High Speed Camera ◽  
Improved Method ◽  
Time Period ◽  
Lip Reading

The authors describe an improved method for detecting distinctive mouth shapes in Japanese utterance image sequences. Their previous method uses template matching. Two types of mouth shapes are formed when a Japanese phone is pronounced: one at the beginning of the utterance (the beginning mouth shape, BeMS) and the other at the end (the ending mouth shape, EMS). The authors’ previous method could detect mouth shapes, but it misdetected some shapes because the time period in which the BeMS was formed was short. Therefore, they predicted that a high-speed camera would be able to capture the BeMS with higher accuracy. Experiments showed that the BeMS could be captured; however, the authors faced another problem. Deformed mouth shapes that appeared in the transition from one shape to another were detected as the BeMS. This study describes the use of optical flow to prevent the detection of such mouth shapes. The time period in which the mouth shape is deformed is detected using optical flow, and the mouth shape during this time is ignored. The authors propose an improved method of detecting the BeMS and EMS in Japanese utterance image sequences by using template matching and optical flow.

Automatic Detection of Crucial Areas Based on Speed Correlation in Video Sequences

2013 ◽  
Vol 427-429 ◽  
pp. 1789-1793
Author(s):  
Shuang Jun Liu ◽  
Rong Yi Cui
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Observation Time ◽  
Video Sequences ◽  
Video Frame ◽  
Surveillance Video ◽  
Optical Flow Field ◽  
Video Images ◽  
The Difference ◽  
Examination Room

Based on video frame differential optical flow field, a method of crucial area detection for surveillance video images of examination room is proposed in this paper. Firstly, the optical flow field was calculated with the difference between two adjacent frames. Secondly, the scene was divided roughly into several blocks, and the blocks of which centroid speed is higher than given threshold were further divided into fine sub-blocks, and furthermore, the sub-block which has maximum centroid speed in the block was marked as the area of abnormal target. Finally, the sub-blocks with exceptional speed in the same observation time slice were judged to be the correlate areas with abnormal speed (CAAS), and the intersection of adjacent CAAS were determined as the crucial area. Experimental results show that the proposed method can effectively detect the abnormal movement area, and can accurately position the crucial area affecting other targets movement.

scholarly journals AN ALGORITHM FOR PEDESTRIAN DETECTION IN MULTISPECTRAL IMAGE SEQUENCES

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 73-77 ◽  
Author(s):  
V. V. Kniaz ◽  
V. V. Fedorenko
Keyword(s):  
Optical Flow ◽  
Pedestrian Detection ◽  
Obstacle Detection ◽  
Multispectral Image ◽  
Low Light ◽  
Detection Algorithms ◽  
Optical Flows ◽  
The Difference ◽  
Self Driving Cars

The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

scholarly journals Enhanced Real-Time Intermediate Flow Estimation for Video Frame Interpolation

2021 ◽  
Author(s):  
Minseop Kim ◽  
Haechul Choi
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Estimation Method ◽  
Frame Rate ◽  
Video Frame ◽  
High Quality ◽  
Frame Interpolation ◽  
Real Time Processing ◽  
High Frame Rate ◽  
Flow Estimation

Recently, the demand for high-quality video content has rapidly been increasing, led by the development of network technology and the growth in video streaming platforms. In particular, displays with a high refresh rate, such as 120 Hz, have become popular. However, the visual quality is only enhanced if the video stream is produced at the same high frame rate. For the high quality, conventional videos with a low frame rate should be converted into a high frame rate in real time. This paper introduces a bidirectional intermediate flow estimation method for real-time video frame interpolation. A bidirectional intermediate optical flow is directly estimated to predict an accurate intermediate frame. For real-time processing, multiple frames are interpolated with a single intermediate optical flow and parts of the network are implemented in 16-bit floating-point precision. Perceptual loss is also applied to improve the cognitive performance of the interpolated frames. The experimental results showed a high prediction accuracy of 35.54 dB on the Vimeo90K triplet benchmark dataset. The interpolation speed of 84 fps was achieved for 480p resolution.

scholarly journals A Driver’s Visual Attention Prediction Using Optical Flow

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3722
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Visual Attention ◽  
Optical Flow ◽  
Prediction Models ◽  
Apparent Movement ◽  
Image Sequences ◽  
Motion Information ◽  
Accuracy Improvement ◽  
Flow Maps ◽  
Current State ◽  
Motion Features

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

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