The Detection and Tracking of Weak Frequency Line Based on Double-detection Algorithm

2007 ◽  
Author(s):  
Mingzhi Lu ◽  
Meng Li ◽  
Weining Mao
Keyword(s):  
Detection Algorithm ◽  
Detection And Tracking

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

scholarly journals A characterisation of Alpine mesocyclone occurrence

2021 ◽  
Vol 2 (4) ◽  
pp. 1225-1244
Author(s):  
Monika Feldmann ◽  
Urs Germann ◽  
Marco Gabella ◽  
Alexis Berne
Keyword(s):  
Radar Data ◽  
Detection Algorithm ◽  
Data Set ◽  
Additional Information ◽  
Detection And Tracking ◽  
Synoptic Weather ◽  
Radar Network ◽  
Southern Side ◽  
The Alps ◽  
Insight Into

Abstract. This work presents a characterisation of mesocyclone occurrence and frequency in the Alpine region, as observed from the Swiss operational radar network; 5 years of radar data are processed with a thunderstorm detection and tracking algorithm and subsequently with a new mesocyclone detection algorithm. A quality assessment of the radar domain provides additional information on the reliability of the tracking algorithms throughout the domain. The resulting data set provides the first insight into the spatiotemporal distribution of mesocyclones in the Swiss domain, with a more detailed focus on the influence of synoptic weather, diurnal cycle and terrain. Both on the northern and southern side of the Alps mesocyclonic signatures in thunderstorms occur regularly. The regions with the highest occurrence are predominantly the Southern Prealps and to a lesser degree the Northern Prealps. The parallels to hail research over the same region are discussed.

Research on athlete training behavior based on improved support vector algorithm and target image detection

2020 ◽  
Vol 39 (4) ◽  
pp. 5725-5736
Author(s):  
Jiang Min
Keyword(s):  
Target Detection ◽  
Training Model ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Target Image ◽  
Classification Result ◽  
Detection And Tracking ◽  
Neural Network Algorithm ◽  
Fully Connected

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

DEEP LEARNING TECHNIQUE - BASED DRONE DETECTION AND TRACKING

2021 ◽  
pp. 10-19
Author(s):  
Xuan Tung Truong
Keyword(s):  
Deep Learning ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Single Shot ◽  
Complex Environments ◽  
Security Risks ◽  
Security Breaches ◽  
Detection And Tracking ◽  
Illegal Activities ◽  
Fine Tune

The usage of small drones/UAVs is becoming increasingly important in recent years. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. This paper resolves the problem of detecting small drones in surveillance videos using deep learning algorithms. Single Shot Detector (SSD) object detection algorithm and MobileNet-v2 architecture as the backbone were used for our experiments. The pre-trained model was re-trained on custom drone synthetic dataset by using transfer learning’s fine-tune technique. The results of detecting drone in our experiments were around 90.8%. The combination of drone detection, Dlib correlation tracking algorithm and centroid tracking algorithm effectively detects and tracks the small drone in various complex environments as well as is able to handle multiple target appearances.

scholarly journals Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
Yang Jie ◽  
LilianAsimwe Leonidas ◽  
Farhan Mumtaz ◽  
Munsif Ali
Keyword(s):  
Clustering Algorithm ◽  
Mutual Exclusion ◽  
Detection Algorithm ◽  
Inland Waterways ◽  
Softmax Classifier ◽  
Inland Navigation ◽  
Ship Detection ◽  
Detection And Tracking ◽  
Complex Scenes ◽  
Real Time Tracking

Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents.

scholarly journals A Characterisation of Alpine Mesocyclone Occurrence

2021 ◽  
Author(s):  
Monika Feldmann ◽  
Urs Germann ◽  
Marco Gabella ◽  
Alexis Berne
Keyword(s):  
Temporal Distribution ◽  
Radar Data ◽  
Detection Algorithm ◽  
Data Set ◽  
Additional Information ◽  
Detection And Tracking ◽  
Synoptic Weather ◽  
Southern Side ◽  
The Alps ◽  
Spatio Temporal

Abstract. This work presents a characterization of mesocyclone occurrence and frequency in the Alpine region, as observed from the Swiss operational radar network. Five years of radar data are processed with a thunderstorm detection and tracking algorithm and subsequently with a mesocyclone detection algorithm. A quality assessment of the radar domain provides additional information on the reliability of the tracking algorithms throughout the domain. The resulting data set provides the first insight into the spatio-temporal distribution of mesocyclones in the Swiss domain, with a more detailed focus on the influence of synoptic weather, diurnal cycle and terrain. Both on the northern and southern side of the Alps mesocyclonic signatures in thunderstorms are frequent. The regions with highest occurrence are predominantly the Southern Prealps and to a lesser degree the Northern Prealps. The parallels to hail research over the same region are discussed.

Moving Object Detection and Tracking Using Particle Filter

2013 ◽  
Vol 321-324 ◽  
pp. 1200-1204 ◽  
Author(s):  
M.M. Naushad Ali ◽  
M. Abdullah-Al-Wadud ◽  
Seok Lyong Lee
Keyword(s):  
Particle Filter ◽  
Detection Algorithm ◽  
Human Motion ◽  
Human Detection ◽  
Corner Detection ◽  
Feature Points ◽  
Detection And Tracking ◽  
Harris Corner ◽  
Challenging Tasks ◽  
Non Gaussian

Moving human detection and tracking are challenging tasks in computer vision. Human motion is usually non-linear and non-Gaussian, and thus many common algorithms are not appropriate for tracking. In this paper we propose a robust tracking algorithm based on particle filter. Multiple moving human in a video sequence are detected using frame difference and morphological operation. Then feature points of every person are extracted using a Harris Corner detection algorithm. Finally, Histogram of Oriented Gradient (HOG) is calculated for each feature point and feature points of the corresponding person are tracked using particle filter. Experimental results demonstrate that our method is efficient to improve the performance of tracking.

scholarly journals Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot

2021 ◽  
Vol 11 (13) ◽  
pp. 6229
Author(s):  
Jong-Ho Han ◽  
Hyun-Woo Kim
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection And Tracking ◽  
Lane Recognition ◽  
Fundamental Requirement ◽  
Three Dimensional Space

This paper proposes a lane detection algorithm using a laser range finder (LRF) for the autonomous navigation of a mobile robot. There are many technologies for ensuring the safety of vehicles, such as airbags, ABS, and EPS. Further, lane detection is a fundamental requirement for an automobile system that utilizes the external environment information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. In the case of a vision-based system, the recognition of the environment of a three-dimensional space becomes excellent only in good conditions for capturing images. However, there are so many unexpected barriers, such as bad illumination, occlusions, vibrations, and thick fog, that the vision-based method cannot be used for satisfying the abovementioned fundamental requirement. In this paper, a three-dimensional lane detection algorithm using LRF that is very robust against illumination is proposed. For the three-dimensional lane detection, the laser reflection difference between the asphalt and the lane according to color and distance has been utilized with the extraction of feature points. Further, a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been experimentally verified.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

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