LITERATURE SURVEY ON CAR AND PEDESTRIAN TRACKING SYSTEM

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
B W. Balkhande ◽  
Rohit Chaurasia ◽  
Pallavi Dhumal ◽  
Kirti Gupta
Keyword(s):  
Computer Vision ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Tracking System ◽  
Human Robot Interaction ◽  
Data Sets ◽  
Vehicle Speed ◽  
Pedestrian Tracking ◽  
Traffic Flow Prediction ◽  
Detection Algorithms

This project focuses on Car and Pedestrian Tracking System using Python. We are using OpenCV (Computer Vision) and HAAR Cascade Classifier Algorithm (Machine Learning Xml files). Since we are tracking both car and pedestrian using computer vision, we have considered two data sets one for car and one for pedestrian with both positive and negative images. We are using the object detection algorithms i.e. HAAR for this project. Some of the possible application can be Traffic Safety, Human - Robot Interaction, Surveillance Application and Some application in vehicle detection where it aims to provide information assisting vehicle counting, Vehicle speed Measurements, Identification of traffic accidents, Traffic flow prediction, and is also being used in Tesla Industry for their Auto Drive mode.

Research on Safety Vehicle Distance Recognition Based on Stereo Vision and DSP

2013 ◽  
Vol 333-335 ◽  
pp. 805-810 ◽  
Author(s):  
Rong Bao Chen ◽  
Ning Li ◽  
Hua Feng Xiao ◽  
Wei Hou
Keyword(s):  
Stereo Vision ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Intelligent Transportation ◽  
Research Area ◽  
Measurement Range ◽  
Vehicle Speed ◽  
Target Vehicle ◽  
Operation Rate ◽  
The Right

With the development of economy, there are an increasing number of cars as well as traffic accidents, thus intensifying the need to take measures to reduce traffic accidents and protect the safety of life and property. Vehicle distance is one of the most important indexes of traffic safety. The measurement of safety vehicle distance has become an increasingly hot research area of intelligent transportation. Through analyzing the basic principle of stereo vision and calibrating the parameters of the CCD sensors both inside and outside, this paper comes up with a method to measure the former vehicle distance based on stereo vision and DSP. Once the vehicle speed and distance form a non-security association, it will give a warning, and upload data or force speed-limiting. According to the different coordinates of the obtained images of the target vehicle from the left and the right sensor, this method can identify feature points, calculate distance to the target vehicle, and analyze the security of vehicle distance. The experimental results show that this method has wide measurement range, high measurement accuracy, and fast operation rate, thus it can meet the actual needs of the measurement of safe vehicle distance in intelligent transportation.

Anti-Rear-End Collision Warning System Based-on Zigbee for Highway Vehicle

2011 ◽  
Vol 130-134 ◽  
pp. 3511-3514
Author(s):  
Wen Jun Li ◽  
Kui Feng ◽  
Hong Kun Zhang
Keyword(s):  
Traffic Safety ◽  
Early Warning ◽  
Traffic Accidents ◽  
Warning System ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Collision Warning ◽  
Distance Model ◽  
Pretreatment Time

The majority of traffic accidents on the highway is rear-end collision. According to statistics about ninety percent rear-end collision can be avoided if there is 1 second pretreatment time for drivers. So it is necessary to develop automotive rear-end collision warning system to prevent the occurrence of accidents. The anti-rear-end collision warning system based on Zigbee technology is designded in this paper. Vehicle speed, acceleration and other traffic information among the nodes can be exchanged on real-time by using self-organizing wireless sensor networks consisted by Zigbee network nodes. A vehicle safe distance model is established after considered the effects of other traffic safety factors, and then, the actual distance measured by radar is compared with the safe distance so as to provide early warning, alarm and other driving information to drivers. The experiment results show that the system can effectively provide early warning, avoid rear-end collision, and improve active safety of vehicles.

scholarly journals Validation of an Assistance System for Merging Maneuvers in Highways in Real Driving Conditions

2019 ◽  
Vol 18 (6) ◽  
pp. 525-531
Author(s):  
S. Sanchez-Mateo ◽  
E. Perez-Moreno ◽  
F. Jimenez ◽  
F. Serradilla ◽  
A. Cruz Ruiz ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Human Error ◽  
Traffic Accidents ◽  
Tracking System ◽  
Assistance System ◽  
Highway Traffic ◽  
Driver Assistance ◽  
National Highway Traffic Safety ◽  
Attentional Load ◽  
Driving Conditions

In the latest study conducted by the National Highway Traffic Safety Administration in 2018, it was published that human error is still considered the major factor in traffic accidents, 94 %, compared with other causes such as vehicles, environment and unknown critical reasons. Some driving scenarios are especially complex, such as highways merging lanes, where the driver obtains information from the environment while making decisions on how to proceed to perform the maneuver smoothly and safely. Ignorance of the intentions of the drivers around him leads to risky situations between them caused by misunderstandings or erroneous assumptions or perceptions. For this reason, Advanced Driver Assistance Systems could provide information to obtain safer maneuvers in these critical environments. In previous works, the behavior of the driver by means of a visual tracking system while merging in a highway was studied, observing a cognitive load in those instants due to the high attentional load that the maneuver requires. For this reason, a driver assistance system for merging situations is proposed. This system uses V2V communications technology and suggests to the driver how to modify his speed in order to perform the merging manoeuver in a safe way considering the available gap and the relative speeds between vehicles. The paper presents the results of the validation of this system for assisting in the merging maneuver. For this purpose, the interface previously designed and validated in terms of usability, has been integrated into an application for a mobile device, located inside the vehicle and tests has been carried out in real driving conditions.

scholarly journals Relationship between Speed Characteristics and Traffic Safety on Freeways in Mainland China

2018 ◽  
Vol 47 (4) ◽  
pp. 318-328 ◽  
Author(s):  
Ziyue Tang
Keyword(s):  
Standard Deviation ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Mainland China ◽  
Practical Significance ◽  
Vehicle Speed ◽  
Average Speed ◽  
Regression Methods ◽  
Nonlinear Regression Method ◽  
Speed Standard Deviation

Based on the traffic accidents statistical data of 10 typical freeways in mainland China, by using of some kinds of regression model, the influences of the average vehicle speed and the speed standard deviation on the traffic safety are studied. According to the regression results, the accidents show an increasing trend with the increase of the vehicle average speed and the speed standard deviation. On this basis, in view of the regression results, the strategy is put forward for controlling the vehicle average speed and the speed standard deviation, which has important theoretical and practical significance for improving highway safety. After a comprehensive comparison among these regression methods, it is found that the nonlinear regression method of user-defined model expression has the best fitting effect, and it can also more accurately describe the objective reality. It has high practicality and popularized value.

scholarly journals Effectiveness of Pedestrian Safety Service Provision Using Sensing Technology

2021 ◽  
Vol 13 (16) ◽  
pp. 9333
Author(s):  
Ki-Man Hong ◽  
Sang-Hoon Son ◽  
Jong-Hoon Kim
Keyword(s):  
Traffic Safety ◽  
Child Protection ◽  
Traffic Accidents ◽  
Pedestrian Safety ◽  
Vehicle Speed ◽  
Protection Zone ◽  
Road Section ◽  
Sensing Technology ◽  
Approach Speed ◽  
Pedestrian Traffic

In this study, we describe the results of an analysis of the effectiveness of providing pedestrian safety services, in terms of reducing pedestrian traffic accidents. We conducted our analysis by investigating the speed of vehicles at two different demonstration points, where the same system and service were provided. For this purpose, we selected a child protection zone and a point on a general road section where a raised crossing is installed. We conducted vehicle speed surveys at the point adjacent to the crosswalk and the points where the driver is expected to be fully provided with information, in order to examine the change in vehicle approach speed, depending on the provision of the service. Overall, the analysis showed that the vehicle’s speed at the point and approaching speed decreased when the pedestrian safety service was provided; however, the effect was more pronounced in the child protection zone, considering the characteristics of the demonstration points. From these results, we conclude that it is necessary to provide services and develop guidelines considering the surrounding environment, such as traffic safety facilities and road safety facilities, according to the characteristics and classification of each point, in order to provide efficient pedestrian safety services.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals Behavioral Dynamics of Pedestrians Crossing between Two Moving Vehicles

2020 ◽  
Vol 10 (3) ◽  
pp. 859 ◽  
Author(s):  
Soon Ho Kim ◽  
Jong Won Kim ◽  
Hyun-Chae Chung ◽  
Gyoo-Jae Choi ◽  
MooYoung Choi
Keyword(s):  
Traffic Accidents ◽  
Vehicle Speed ◽  
Mathematical Tool ◽  
Vehicle Type ◽  
Behavioral Dynamics ◽  
Moving Vehicles ◽  
Bearing Angle ◽  
Human Participants ◽  
Crossing Point ◽  
Insight Into

This study examines the human behavioral dynamics of pedestrians crossing a street with vehicular traffic. To this end, an experiment was constructed in which human participants cross a road between two moving vehicles in a virtual reality setting. A mathematical model is developed in which the position is given by a simple function. The model is used to extract information on each crossing by performing root-mean-square deviation (RMSD) minimization of the function from the data. By isolating the parameter adjusted to gap features, we find that the subjects primarily changed the timing of the acceleration to adjust to changing gap conditions, rather than walking speed or duration of acceleration. Moreover, this parameter was also adjusted to the vehicle speed and vehicle type, even when the gap size and timing were not changed. The model is found to provide a description of gap affordance via a simple inequality of the fitting parameters. In addition, the model turns out to predict a constant bearing angle with the crossing point, which is also observed in the data. We thus conclude that our model provides a mathematical tool useful for modeling crossing behaviors and probing existing models. It may also provide insight into the source of traffic accidents.

scholarly journals Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3536
Author(s):  
Jakub Górski ◽  
Adam Jabłoński ◽  
Mateusz Heesch ◽  
Michał Dziendzikowski ◽  
Ziemowit Dworakowski
Keyword(s):  
Novelty Detection ◽  
Feature Space ◽  
Detection Methods ◽  
Data Sets ◽  
Similar Distribution ◽  
Detection Algorithms ◽  
Rotary Machinery ◽  
Detection Approach ◽  
Input Reconstruction ◽  
Quantitative Indicators

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

scholarly journals Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Bingsheng Wei ◽  
Martin Barczyk
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Tracking System ◽  
Ground Truth ◽  
White Background ◽  
Cascade Classifier ◽  
Detection Algorithms ◽  
Squared Error ◽  
Test Conditions ◽  
Video Feed

We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system.

scholarly journals Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lei Lin ◽  
Feng Shi ◽  
Weizi Li
Keyword(s):  
New York ◽  
Los Angeles ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Human Mobility ◽  
Critical Conditions ◽  
Road Traffic Safety ◽  
Accident Data ◽  
The Impact

AbstractCOVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

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