Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image

Analysis and recognition of ancient scripts is a challenging task as these scripts are inscribed on pillars,stones,or leaves.Optical recognition systems can help in preserving,sharing,and accelerate the study of the ancient scripts,but lack of standard dataset for such scripts is a major constraint.Although many scholars and researchers have captured and uploaded inscription images on various websites,manual searching,downloading and extraction of these images is tedious and error prone.Web search queries return a vast number of irrelevant results,and manually extracting images for a specific script is not scalable.This paper proposes a novelmultistage system to identify the specific set of script images from a large set of images downloaded from web sources.The proposed system combines the two most important pattern matching techniques-Scale Invariant Feature Transform(SIFT)and Template matching,in a sequential pipeline,and by using the key strengths of each technique,the system can discard irrelevant images while retaining a specific type of images.

TEMPLATE MATCHING ALGORITHM OF RADAR BEAM SCAN TYPE RECOGNITION

Phased array radar has been applied broadly because of its sound performance.But signal of phased array radar is of a wide variety of types.Therefore,recognition of phased array radar is the most puzzling aspect of the whole emitter identification domain.To solve the problem,the article proposes the method that identifies phased array radar by pulse amplitude information,and studies the phased array radar,models transmit signal of them,and receiving signal by radar countermeasure reconnaissance receiver.From constructing template of pulse train's amplitude vector of mechanical scanning radar,computing distance of samples and standard template,finding threshold of the template matching arithmetic,the article puts forward the template matching algorithm of radar beam scan type recognition to identify phased array radar automatically.

An effective method for laboratory acoustic emission detection and location using template matching

In this paper, a template matching and location method, which has been rapidly adopted in microseismic research in recent years, is applied to laboratory acoustic emission(AE) monitoring. First, we used traditional methods to detect P-wave first motions and locate AE hypocenters in three dimensions. In addition, we selected events located with sufficient accuracy(normally corresponding AE events of relatively larger energy, showing clear P-wave first motion and a higher signal-to-noise ratio in most channels) as template events. Then, the template events were used to scan and match other poorly located events in triggered event records or weak events in continuous records. Through crosscorrelation of the multi-channel waveforms between the template and the event to be detected, the weak signal was detected and located using a grid-searching algorithm(with the grid centered at the template hypocenter). In order to examine the performance of the approach, we calibrated the proposed method using experimental data of different rocks and different types of experiments. The results show that the proposed method can significantly improve the detection capability and location accuracy, and can be applied to various laboratory and in situ experiments, which use multi-channel AE monitoring with waveforms recorded in either triggering or continuous mode.

Template matching for simple waveforms with low signal-to-noise ratio and its application to icequake detection

Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal function.In this study,we modify the traditional template matching approach for this situation.We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations.Next,we select a template event from the virtual station,and apply the traditional template matching.We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching method.It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs.

Hierarchical Template Matching for Robust Visual Tracking with Severe Occlusions

To tackle the problem of severe occlusions in visual tracking, we propose a hierarchical template-matching method based on a layered appearance model. This model integrates holistic- and part-region matching in order to locate an object in a coarse-to-fine manner. Furthermore, in order to reduce ambiguity in object localization, only the discriminative parts of an object＇ s appearance template are chosen for similarity computing with respect to their cornerness measurements. The similarity between parts is computed in a layer-wise manner, and from this, occlusions can be evaluated. When the object is partly occluded, it can be located accurately by matching candidate regions with the appearance template. When it is completely occluded, its location can be predicted from its historical motion information using a Kalman filter. The proposed tracker is tested on several practical image sequences, and the experimental results show that it can consistently provide accurate object location for stable tracking, even for severe occlusions.

Research on the measurement of belt speed by video in coal mine based on improved template matching algorithm

In order to improve the intelligence of video monitoring system of belt and make up the deficiency of higher failure rate and bad real-time performance in the traditional systems of measurement of belt speed, according to the fact that the light of coal mine is uneven, the strength of light changes greatly, the direction of belt movement is constant, and the position of camera was fixed, various algorithms of speed measurement by video were studied, and algorithm for template matching based on sum of absolute differences （SAD） and correlation coefficient was proposed and improved, besides, the tracking of feature regions was realized. Then, a camera calibration method using the invariance of the cross-ratio was adopted and the real-time measurement of belt speed by the hardware platform based on DM642 was realized. Finally, experiment results show that this method not only has advantages of high precision and strong anti-jamming capability but also can real-time reflect the changes of belt speed, so it has a comprehensive applicability.

SYMBOL-RATE ESTIMATION BASED ON TEMPLATE MATCHING RULES

For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.

Accelerated Generation of Spatiotemporal Atlas through Fast Template Matching

A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.

Fingerprints Matching Based on the Integrated Template

Traditional fingerprints matching approaches are based on the minutiae and the texture characteristics. It is difficult for today's techniques to deal with the images captured by the solid-state fingerprint sensors because the sampling area of these sensors is very small and only portion of the fingerprint can be obtained. In this paper, a novel integrated template has been built using several images and combining the minutiae with the texture characteristics at the same time. By using a kind of Gabor filters (eight directions) on the region around the reliable minutiae, local texture of each minutia termed as minutiacode is extracted. Further, a real-time matching algorithm using the integrated template is presented. The output is determined by the weight factor of the compound matching. Finally, experimental results show that this system performs well in reducing false reject rate (FRR).

A Template Matching Based Feature Extraction for Activity Recognition

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.

Parallel Implementation of the Non-Overlapping Template Matching Test Using CUDA

NIST(National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7 th test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit(GPU) using NVIDIA Compute Unified Device Architecture(CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.

Template Matching using Statistical Model and Parametric Template for Multi-Template

This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.

A Robust Template Matching Algorithm Based on Reducing Dimensions

Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative for 2-D full search block matching algorithms. Our approach consists of three steps. In the first step the images are converted from 2-D into 1-D by summing up the intensity values of the image in two directions horizontal and vertical. In the second step, the template matching is performed among 1-D vectors using the similarity function sum of square difference. Finally, the decision will be taken based on the value of similarity function. Transformation template image and sub-images in the source image from 2-D grey level information into 1-D information vector reduce the dimensionality of the data and accelerate the computations. Experimental results show that the computational time of the proposed approach is faster and performance is better than three basic template matching methods. Moreover, our approach is robust to detect the target object with changes of illumination in the template also when the Gaussian noise added to the source image.

Template Matching from 2-D into 1-D

The following article has been retracted due to special reason of the authors. This paper published in Vol.5 No. 2, 2014, has been removed from this site. Title: Template Matching from 2-D into 1-D Author: Yasser

Learning accurate template matching with differentiable coarseto-fine correspondence refinement

Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream tasks such as robotic grasping.Existing methods fail when the template and source images have different modalities,cluttered backgrounds,or weak textures.They also rarely consider geometric transformations via homographies,which commonly exist even for planar industrial parts.To tackle the challenges,we propose an accurate template matching method based on differentiable coarse-tofine correspondence refinement.We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image,allowing robust matching.An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers.This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation.Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines,providing good generalization ability and visually plausible results even on unseen real data.

Template Match Object Detection for Inertial Navigation Systems

This paper devoted to propose template match object detection for inertial navigation systems (INS). The proposed method is an image processing technique to improve the precision of the INS for detecting and tracking the ground objects from flying vehicles. Template matching is one of the methods used for ground object detection and tracking. Robust and reliable object detection is a critical step of object recognition. This paper presents a proposed mathematical morphological template matching method for detection and tracking of ground objects. Our focus is on flying systems equipped with camera to capture photos for the ground and recognize it. The proposed method is independent on the altitude or the orientation of the object. The algorithm is simulated using Matlab program and the numerical experiments are shown which verify the object detection for a wide range altitude and orientation. The results show superiority of this method for identifying and recognizing the ground objects.

A multiple template approach for robust tracking of fast motion target

Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.

Adaptive template filter method for image processing based on immune genetic algorithm

To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.

A color based face detection system using multiple templates

A color based system using multiple templates was developed and implem ented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the fronta l human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution o f human skin in chromatic color space in order to get a chroma chart showing lik elihoods of skin colors. This chroma chart is used to generate, from the origina l color image, a gray scale image whose gray value at a pixel shows its likelih ood of representing the skin. The algorithm uses an adaptive thresholding proces s to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates ma tching is used to determine if a given skin region represents a frontal human fa ce or not. Test of the system with more than 400 color images showed that the re sulting detection rate was 83%, which is better than most color-based face dete c tion systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.