scholarly journals Multi-Bar Code Recognition Algorithm Based on SM2 Encryption and Decryption of the Motion of the Video Stream

2016 ◽  
Vol 06 (01) ◽  
pp. 58-64
Author(s):  
春才 王
Keyword(s):  
Video Stream ◽  
Recognition Algorithm ◽  
Bar Code ◽  
Encryption And Decryption

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Direct Part Mark Bar Code Image Preprocessing

2015 ◽  
Vol 7 (3) ◽  
pp. 13-24
Author(s):  
Lingling Li ◽  
Tao Gao ◽  
Yaoquan Yang
Keyword(s):  
Recognition Algorithm ◽  
Code Region ◽  
Data Matrix ◽  
Background Region ◽  
Vertical Projection ◽  
Transform Method ◽  
Color Processing ◽  
Low Contrast ◽  
Bar Code ◽  
Intensity Changes

Due to factors such as ambient light and metal materials, the collected industrial DPM bar code images may exist uneven illumination, low contrast, color of background area is darker than bar code region and other harsh issues, while the existing 2D code recognition device can only recognize the type which bar code area color is darker than background region. Therefore, the quality of preprocessing effect is the key point to subsequent recognition algorithm. In this paper, the homomorphic filtering method is used to weaken the influence of uneven illumination firstly, which will enhance the image contrast degree. Then do horizontal and vertical projection, find the points with greater intensity changes in both directions, make the image into blocks, again use the classic Kittler binarization algorithm on each block, then use mathematical morphology method to standardize the dot data matrix images. Finally, an improved Hough transform method is used to detect the ‘L' type finder pattern accurately, then find its pixel value, if color of the background region is darker than the bar code area, do invert-color processing. The processing results of a set of industrial DPM bar code images confirm the effectiveness of the proposed method.

A Gesture Recognition Algorithm Using Hausdorff-Like Distance Template Matching Based on the Main Direction of Gesture

2015 ◽  
Vol 713-715 ◽  
pp. 2156-2159 ◽  
Author(s):  
Xue Wen Yang ◽  
Zhi Quan Feng ◽  
Zhong Zhu Huang ◽  
Na Na He
Keyword(s):  
Coordinate System ◽  
Gesture Recognition ◽  
Template Matching ◽  
Recognition Rate ◽  
Video Stream ◽  
Recognition Algorithm ◽  
Main Direction ◽  
Hand Gesture ◽  
Rectangular Coordinate System ◽  
Image Area

Hand gesture of rotation, scaling and translation is the key problem of gesture recognition. This paper proposes a gesture recognition algorithm based on Hausdorff-like distance template matching of gesture main direction. Firstly, we segment hand gesture from video stream. Secondly, we calculate the main direction of gesture in the image, and build a 2D rectangular coordinate system. Then, we clockwise divide the gesture into eight sub-image area along the main direction of gesture and calculate the coordinates of target pixel points in each sub-image area in the 2D rectangular coordinate system. Finally, the algorithm of Hausdorff-like distance template matching is used to recognize the final gesture. Experimental results show that this algorithm can achieve real-time correct recognition of gestures in relatively stable light conditions. The overall recognition rate can reach 95%.

Robot Color Recognition Based on HSI Color Space

2014 ◽  
Vol 494-495 ◽  
pp. 1016-1019
Author(s):  
Hong Wei Zhao ◽  
Tian Jiao Zhao ◽  
Ding Long He ◽  
Man Li Long
Keyword(s):  
Color Space ◽  
Video Stream ◽  
Recognition Algorithm ◽  
Parameter Setting ◽  
Experiment Platform ◽  
Color Recognition ◽  
Robot Tracking ◽  
Optimization Function ◽  
Hsi Color Space ◽  
Simulation Results

We designed a color recognition algorithm based on HSI color space, through front-facing camera to identify the scene in front of the robot, then video stream was cut into frames and image processing was conducted. The presented algorithm does well on the robot experiment platform, we can carry out the color recognition, and also can identify the size of the red area through the parameter setting, so as to choose the preset area for tracking. Simulation results show that the optimization function has successfully filtered distractors, the system basically meets the requirements of real-time, providing effective support for the robot tracking.

scholarly journals An Efficient Bar Code Image Recognition Algorithm for Sorting System

2020 ◽  
Vol 64 (3) ◽  
pp. 1885-1895
Author(s):  
Desheng Zheng ◽  
Ziyong Ran ◽  
Zhifeng Liu ◽  
Liang Li ◽  
Lulu Tian
Keyword(s):  
Image Recognition ◽  
Recognition Algorithm ◽  
Bar Code ◽  
Sorting System ◽  
Code Image

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

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