A system for real-time detection and tracking of vehicles from a single car-mounted camera

Caraffi, C.; Vojíř, Tomáš; Trefny, Jiri; Šochman, J; Matas, Jiřı́

doi:10.1109/itsc.2012.6338748

Cited by 107 publications

(55 citation statements)

References 30 publications

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“…In [67], online boosting was used to train a vehicle detector. In [68], WaldBoost was used to train the vehicle detector.…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

“…The newly released KITTI database [198] contains extensive video data captured with a calibrated stereo rig, as well as synchronized lidar data, which can be used as a ground truth for vehicle localization. The recently released data set in [68] has also contained lidar data so that vision-based detection accuracy can be evaluated as a function of longitudinal distance. However, ground truth for dynamical parameters such as velocity and vehicle yaw rate must necessarily come from the tracked vehicle's own CAN, which is not feasible outside of controlled and orchestrated trials.…”

Section: Benchmarksmentioning

confidence: 99%

See 1 more Smart Citation

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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“…In [67], online boosting was used to train a vehicle detector. In [68], WaldBoost was used to train the vehicle detector.…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

Section: Benchmarksmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

View full text Add to dashboard Cite

show abstract

“…Single camera or more cameras' images are used with GPS data to enhance the positioning accuracy in many studies. Caraffi et al (2012) presented a system for detection and tracking of vehicles from a single car-mounted camera. Though the system showed high potential for positioning using images from a single camera, had some constraint condition and was far from perfect in terms of automation.…”

Section: Indrodouctionmentioning

confidence: 99%

Improving Car Navigation With a Vision-Based System

Kim

Choi

Lee

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The real-time acquisition of the accurate positions is very important for the proper operations of driver assistance systems or autonomous vehicles. Since the current systems mostly depend on a GPS and map-matching technique, they show poor and unreliable performance in blockage and weak areas of GPS signals. In this study, we propose a vision oriented car navigation method based on sensor fusion with a GPS and in-vehicle sensors. We employed a single photo resection process to derive the position and attitude of the camera and thus those of the car. This image georeferencing results are combined with other sensory data under the sensor fusion framework for more accurate estimation of the positions using an extended Kalman filter. The proposed system estimated the positions with an accuracy of 15 m although GPS signals are not available at all during the entire test drive of 15 minutes. The proposed vision based system can be effectively utilized for the low-cost but high-accurate and reliable navigation systems required for intelligent or autonomous vehicles.

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“…The combination of Haar features and Adaboost classification was used to detect parts of vehicles in [50]. In [51], Waldboost was used to train the vehicle detector.…”

Section: B Appearance: Classificationmentioning

confidence: 99%

A review of recent developments in vision-based vehicle detection

Sivaraman¹,

Trivedi²

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the state-of-the-art, and provide perspective on future research directions in the field.

show abstract

A system for real-time detection and tracking of vehicles from a single car-mounted camera

Cited by 107 publications

References 30 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Improving Car Navigation With a Vision-Based System

A review of recent developments in vision-based vehicle detection

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