scholarly journals TARGET DETECTION AND ANALYSIS OF INTELLIGENT AGRICULTURAL VEHICLE MOVEMENT OBSTACLE BASED ON PANORAMIC VISION

2019 ◽  
Vol 59 (3) ◽  
pp. 277-284 ◽  
Author(s):  
Wu Weibing
Keyword(s):  
Target Detection ◽  
Quantitative Methods ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Tracking Accuracy ◽  
Agricultural Vehicles ◽  
Panoramic Vision ◽  
Wide Range ◽  
Agricultural Vehicle

Agricultural automation and intelligence have a wide range of connotations, involving navigation, image, model, strategy and other engineering disciplines. With the development of modern agriculture are applied in many engineering areas. The operating environment of agricultural vehicles is very complex, especially as they often face obstacles, affecting the intelligent operation of agricultural vehicles. The traditional obstacle detection mostly uses the limited detection algorithm, in the case of which it is difficult to achieve the moving target detection of panoramic vision. In this paper, mean shift algorithm is selected to detect the moving obstacles of intelligent agricultural vehicles, and adaptive colour fusion is introduced to optimize the algorithm to solve the problems of mean shift. In order to verify the effect of the improvement and application of the algorithm, the video image obtained by the intelligent agricultural vehicle is selected for the simulation experiment, and the best combination (- 0.8.0.2) is obtained for the unequal spacing sampling method. In the process of colour selection, the coefficient needs to be adjusted continuously to improve the tracking accuracy of the algorithm. Further it can be seen that when using a variety of different quantitative methods for comparative analysis, the quantitative method of HIS-360 level is determined.

scholarly journals A Robust Tracking-by-Detection Algorithm Using Adaptive Accumulated Frame Differencing and Corner Features

2020 ◽  
Vol 6 (4) ◽  
pp. 25
Author(s):  
Nahlah Algethami ◽  
Sam Redfern
Keyword(s):  
Detection Algorithm ◽  
Image Features ◽  
Corner Detection ◽  
Wide Angle ◽  
Tracking Accuracy ◽  
Range Of Movement ◽  
Illumination Variation ◽  
Detection Techniques ◽  
Wide Range ◽  
Tracking By Detection

We propose a tracking-by-detection algorithm to track the movements of meeting participants from an overhead camera. An advantage of using overhead cameras is that all objects can typically be seen clearly, with little occlusion; however, detecting people from a wide-angle overhead view also poses challenges such as people’s appearance significantly changing due to their position in the wide-angle image, and generally from a lack of strong image features. Our experimental datasets do not include empty meeting rooms, and this means that standard motion based detection techniques (e.g., background subtraction or consecutive frame differencing) struggle since there is no prior knowledge for a background model. Additionally, standard techniques may perform poorly when there is a wide range of movement behaviours (e.g. periods of no movement and periods of fast movement), as is often the case in meetings. Our algorithm uses a novel coarse-to-fine detection and tracking approach, combining motion detection using adaptive accumulated frame differencing (AAFD) with Shi-Tomasi corner detection. We present quantitative and qualitative evaluation which demonstrates the robustness of our method to track people in environments where object features are not clear and have similar colour to the background. We show that our approach achieves excellent performance in terms of the multiple object tracking accuracy (MOTA) metrics, and that it is particularly robust to initialisation differences when compared with baseline and state of the art trackers. Using the Online Tracking Benchmark (OTB) videos we also demonstrate that our tracker is very strong in the presence of background clutter, deformation and illumination variation.

scholarly journals Research on driverless vehicle vision algorithm

2021 ◽  
Vol 336 ◽  
pp. 06001
Author(s):  
Xinchao Liu ◽  
Ying Yan ◽  
Haiyun Gan
Keyword(s):  
Target Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Urban Traffic ◽  
Experimental Results ◽  
Deep Learning Algorithm ◽  
Deep Model ◽  
Traffic Environment ◽  
Vision Algorithm

Obstacle detection in complex urban traffic environment has become an important part of unmanned vehicle optimization, and its complexity brings great challenges to the reliability of unmanned target detection. YOLOv3 in deep learning algorithm has a good detection effect in target detection, but it has certain defects in detecting targets in complex urban traffic environment. In this paper, the spatial pyramid module is added to YOLOv3 to improve the extraction of data features of the deep model. Then, on the basis of optimized network, the target detection algorithm is streamlined by combining layer pruning and channel pruning. The streamlined algorithm is called YOLOv3-SPP3-Tiny. Comparing the experimental results of YOLOv3-SPP3-tiny and YOLOv3 on Street Scenes dataset, the Precision is improved by 2.77%, the average precision (mAP) is increased by 0.87%, the Total BFLOPS is reduced by 94.49%, and the Inference time is reduced by 80.39%. Experimental results show that the model YOLOv3-SPP3-tiny algorithm is more conducive to unmanned object detection in complex urban road environment.

scholarly journals Vehicle Detection in Remote Sensing Image Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liming Zhou ◽  
Chang Zheng ◽  
Haoxin Yan ◽  
Xianyu Zuo ◽  
Baojun Qiao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Small Target ◽  
Remote Sensing Images ◽  
Target Feature ◽  
Original Algorithm ◽  
Wide Range ◽  
Small Targets

Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.

A Detection Algorithm for Projectile Target

2014 ◽  
Vol 1044-1045 ◽  
pp. 972-975
Author(s):  
Zai Fei Shang ◽  
Chun Ping Wang
Keyword(s):  
Target Detection ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Support Vector ◽  
Performance Simulation ◽  
Histogram Of Oriented Gradient ◽  
Mean Shift Algorithm ◽  
Small Target Detection ◽  
Time Performance ◽  
Vector Machines

For consistency of performance in the shape of the projectile targets, a projectile target detection algorithm is presented based on HOG (Histogram of Oriented Gradient) characterization algorithm. First, detecting the bullet image corner, and secondly, by Mean-shift algorithm improves the corner position accuracy and reduces the number of corner points, finally, applying support vector machines to extract the projectile targets. Compared with the traditional small target detection algorithm, the algorithm describes the targets more accurately, along with better real-time performance. Simulation, the projectile target detection rate of over 80% and verify the effectiveness of the algorithm.

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.

scholarly journals Moving Object Detection Technology of Line Dancing Based on Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liyun Liu
Keyword(s):  
Machine Vision ◽  
Object Detection ◽  
Target Detection ◽  
Moving Objects ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Moving Target ◽  
Detection Technology

In this paper, line dancing's moving object detection technology based on machine vision is studied to improve object detection. For this purpose, the improved frame difference for the background modeling technique is combined with the target detection algorithm. The moving target is extracted, and the postmorphological processing is carried out to make the target detection more accurate. Based on this, the tracking target is determined on the time axis of the moving target tracking stage, the position of the target in each frame is found, and the most similar target is found in each frame of the video sequence. The association relationship is established to determine a moving object template or feature. Through certain measurement criteria, the mean-shift algorithm is used to search the optimal candidate target in the image frame and carry out the corresponding matching to realize moving objects' tracking. This method can detect the moving targets of line dancing in various areas through the experimental analysis, which will not be affected by the position or distance, and always has a more accurate detection effect.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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