scholarly journals Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingfeng Huang ◽  
Yage Huang ◽  
Zhiwei Zhang ◽  
Yujie Zhang ◽  
Weijian Mi ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Detection Algorithm ◽  
Container Terminals ◽  
Multitarget Tracking ◽  
Detection And Tracking ◽  
Prevention System ◽  
Accident Detection ◽  
Vision Detection ◽  
Lifting Equipment ◽  
Traditional Image

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

scholarly journals Application of IOT for Human Safety on Road

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Tripura Pidikiti , Et. al.
Keyword(s):  
Global Positioning System ◽  
Positioning System ◽  
Infrared Sensor ◽  
Road Accidents ◽  
Emergency Center ◽  
Global Positioning ◽  
Prevention System ◽  
Alcohol Sensor ◽  
Two Wheeler ◽  
Accident Detection

Two wheelers (motor bikes) are most used easy and economic means of transportation and it also has become unsafe because of the tremendous increase of road accidents. When two-wheeler met with an accident, it is difficult to spot the neighborhood of the accident and mammoth loss occurs due to time factor. This paper presents Internet of Things based accident detection and prevention system. This is a novel system divided into four parts: first to identify the accident to send signal to emergency center along with location using Arduino based Global Positioning System and Global System for Mobile Communication and remaining are to warn to prevent the accidents like an accelerometer to determine the velocity and tilt of the vehicle, Infrared sensor to detect any obstacles and an alcohol sensor.

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 Public Opinion Analysis of Road Accidents & Review of Conventional and Combinational Driver Safety Methodologies with SelfLearning Algorithms

2021 ◽  
Vol 12 (2) ◽  
pp. 3245-3260
Author(s):  
Tamilarasi A, Et. al.
Keyword(s):  
Public Opinion ◽  
Tamil Nadu ◽  
Detection System ◽  
Detection Algorithm ◽  
Driver Assistance ◽  
Accurate Analysis ◽  
Fitting Algorithm ◽  
Tamil Nadu State ◽  
Curvature Estimation ◽  
Accident Detection

Advanced driver assistance and accident detection system is significantlyneeded to ensure safety for drivers. Drowsiness detection, collision detection and various driver alert systems have penetrated into market with an aim to provide higher security for driver but due to population of vehicle and modification in structure of roads these system fails to answer safety problems that results in severe accidents.In this paper we provide accurate analysis of past recorded accidents in Tamil Nadu state and analysis of public opinion on Accident detection system is carried out using 1004 licensed persons under different ages in three cities (Coimbatore, Erode and Nilgiris) by focusing the major 10 parameters carrying 48 Questions. Findings and implications of this analysis is also discussed in this article. A thorough analysis of recent techniques that are used for AAD (Automatic Accident Detection) and road safety programmes that resolve the pre and post cautionary concerns of accidents in developing countries is addressed with the review of most 4 influencing algorithms in ITS for AAD.. 1 Vehicle Detection using Wheel arc Counter Detection Algorithm, 2 Enhancement of V2X Communication using Multi-RAT,3Road Curvature Estimation using Circle Fitting Algorithm and4Driver Safety Systemis discussed in this paper. To understand recent computational challenges and extended areas of research in ITS, anhybrid approach of CNN with VANETs for accident detection has been suggested to enumerate the obtained accidental information.

scholarly journals FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing

2021 ◽  
Author(s):  
Botao He ◽  
Haojia Li ◽  
Siyuan Wu ◽  
Dong Wang ◽  
Zhiwei Zhang ◽  
...  
Keyword(s):  
Depth Sensing ◽  
Detection And Tracking ◽  
Dynamic Vision ◽  
Dynamic Objects ◽  
Vision Detection ◽  
Fast Dynamic

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.

scholarly journals The Design of Accident Detection and Tracking Systems on Motorcycles

2021 ◽  
pp. 30-36
Author(s):  
Febi Ramadaniati
Keyword(s):  
Sensor Response ◽  
Tracking Systems ◽  
Google Maps ◽  
Detection And Tracking ◽  
Accident Detection ◽  
Formula Method ◽  
Response Area ◽  
Actual Location ◽  
Google Apps

Motorcycles are type of vehicles with the highest accidents percentage every year. The location away from the settlement and the time of the accident is one of the factors that slow down the spread of information about accidents. This study aims to make a tool that can send such information in the form of notifications and crash site points to the victims’ families. Testing was conducted by looking at the sensor response detecting tilt on the motorcycle against the large reading angle. Furthermore, GPS module accuracy testing is conducted in reading the location by comparing GPS module readings with actual location points in Google apps Maps which is then calculated by using the Haversine Formula method up to gsm module capability testing for send SMS notifications in the form of latitude, longitude, and links that can connect to the Google Maps app. Based on the results of the trials and analyses that have been conducted, the slope sensor response area to transmit crash notification is <=50° or >=50°. After that, the GSM module will send notifications and location points detected by the GPS module with an average reading difference of 2.97 meters.

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.

Design of vision detection algorithm and system for BGA welding balls

2018 ◽  
Vol 26 (9) ◽  
pp. 2190-2197
Author(s):  
罗志伟 LUO Zhi-wei ◽  
杨玉龙 YANG Yu-long ◽  
李志红 LI Zhi-hong
Keyword(s):  
Detection Algorithm ◽  
Vision Detection
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