A Hidden Markov Model for Vehicle Detection and Counting

Miller, Nicholas; Thomas, Mohan A.; Eichel, Justin A.; Mishra, Akshaya

doi:10.1109/crv.2015.42

Cited by 21 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PreCrash problem of Intelligent Control of autonomous vehicles robot; Driverless car 100 km experiment Cyber Attack in V2X; Manage low level vehicle actuators (steering throttle and brake); Road and Obstacle Detection; [39], [40], [54], [49], [23] Hidden Markov Based Models 4 7% Vehicle Detection and Counting; Road junction detection; Learn from Demonstration; [36], [29], [45] Estimation Filters 4 7%…”

Section: %mentioning

confidence: 99%

“…Pre-crash problem of Intelligent Control of autonomous vehicles robot; Pedestrian Detection; Providing road safety to connected drivers and connected autonomous vehicles; [39], [26], [19] Adaptive Boosting (AdaBoost) 3 5% Vehicle Detection and Counting; Leading vehicle recogV nition in platooning; Road junction detection; [36], [21], [29] Ramer-Douglas Peucker or Ramer-Douglas algorithm 3 5% Obstacle clustering and tracking; Path tracking; [22], [48] Haar-like feature detector…”

Section: %mentioning

confidence: 99%

“…Developing remotecontrolled car with some automation to deal with traffic light detection, obstacle avoidance system and lane detection system to be driven from anywhere over a secured internet connection; Vehicle Detection and Counting; How to "automate" manual annotation for images to train visual perception for AVs; [38], [36], [37] Histogram of Oriented Gradient (HOG)…”

Section: %mentioning

confidence: 99%

“…The proposed method was also more reliable having fewer and less severe occurrences of 5-minute-bin errors throughout the testing set. [36] Safety-warning and driver-assistance system and an automatic pilot for rural and urban traffic environments Adaptive randomized HT (RHT) for robust and accurate detection of lane markings without manual initialization or a priori information under different road environments…”

Section: Road and Obstacle Detection Sensor Fusion Fuzzy Logicmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

show abstract

Section: %mentioning

confidence: 99%

Section: %mentioning

confidence: 99%

Section: %mentioning

confidence: 99%

Section: Road and Obstacle Detection Sensor Fusion Fuzzy Logicmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…e time headway is the time between successive vehicles as the same point on each vehicle passes a point on a lane or roadway [1]. e time headway is typically acquired by human observation, detectors [3,4], or video detection [5][6][7]. e space headway is obtained from the product of the time headway and the instantaneous speed of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

MPP: A Novel Algorithm for Estimating Vehicle Space Headways from a Single Image

Jiang

2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

Vehicle space headway, also called spacing, is an important and basic traffic parameter. Traditional space headway calculation methods are facing the problems of large errors and high costs. This paper presents a novel algorithm based on measurement point pairs (MPPs) to estimate the real-time microcosmic vehicle space headway from single images in existing traffic surveillance videos and images without any additional equipment. First, the camera is calibrated with road markings to obtain the relationship between the image coordinates and the world coordinates. Second, vehicle pairs of two successive vehicles in the image are established, measurement points on each vehicle are selected by video intelligence analysis technologies, and their world coordinates are calculated by camera calibration results. Finally, the measurement points of the preceding and following vehicles are matched to obtain the MPPs, followed by the calculation of the weighted space headway. By using the measurement point information, one of the most difficult problems in image distance measurement, the lack of height information, is solved. The main factors causing estimation errors are fully addressed and the range and trend of errors under certain conditions are given by virtual simulation. Two real-world experiments are used to prove the accuracy and usability of the MPP in common video scenes: the simulation experiment indicates that the MPP algorithm achieves a high accuracy with estimation error less than ±0.1 m and the relative error within 1.1%; the application experiment shows that the MPP-based calculation is more accurate and stable than the state-of-the-art distance measurement algorithm and that the convenience of the proposed MPP algorithm is higher than that of traditional methods of space headway estimation.

show abstract

Realtime Computer Vision-Based Accurate Vehicle Counting and Speed Estimation for Highways

Shiranthika

Premaratne

Zheng

et al. 2019

Intelligent Computing Theories and Application

View full text Add to dashboard Cite

A Hidden Markov Model for Vehicle Detection and Counting

Cited by 21 publications

References 9 publications

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

MPP: A Novel Algorithm for Estimating Vehicle Space Headways from a Single Image

Realtime Computer Vision-Based Accurate Vehicle Counting and Speed Estimation for Highways

Contact Info

Product

Resources

About