Lateral vehicles detection using monocular high resolution cameras on TerraMax™

Broggi, Alberto; Cappalunga, A.; Cattani, Silvia; Zani, Paolo

doi:10.1109/ivs.2008.4621308

Cited by 22 publications

(14 citation statements)

References 2 publications

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“…The combined parts are tracked using Kalman filtering. In [23], the camera was similarly mounted on the side of the TerraMax autonomous experimental vehicle test-bed. An adaptive background model of the scene was built, and motion cues were used to detect vehicles in the side view.…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

“…Adaptive background models have been used in some studies, in an effort to adapt surveillance methods to the dynamic on-road environment. In [23], an adaptive background model was constructed, with vehicles detected based on motion that differentiated them from the background. Adaptive background modeling was also used in [87], specifically to model the area where overtaking vehicles tend to appear in the camera's field of view.…”

Section: ) Motion-based Approachesmentioning

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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Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

Section: ) Motion-based Approachesmentioning

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

“…In general, these methods has been less common than features-based methods. In [2], [22], adaptive background modelling was used, with vehicles detected based on motion that differentiated them from the background. Optical flow [23], a fundamental machine vision tool, has been used for monocular vehicle detection [24].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

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Abstract-On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.

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“…Adaptive background models have also been used in autonomous vehicles in an effort to adapt surveillance methods to the dynamic on-road environment. In [5], an adaptive background model was constructed, with vehicles detected based on motion that differentiated them from the background. Dynamic modeling of the scene background in the area of the image where vehicles typically overtake was implemented in [6].…”

Section: Introductionmentioning

confidence: 99%

Motion detection using binocular image flow in dynamic scenes

Min

2016

EURASIP J. Adv. Signal Process.

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Motion detection is a hard task for intelligent vehicles since target motion is mixed with ego-motion caused by moving cameras. This paper proposes a stereo-motion fusion method for detection of moving objects from a moving platform. A 3-dimensional motion model integrating stereo and optical flow has been established to estimate the ego-motion flow. The mixed flow is calculated from an edge-indexed correspondence matching algorithm. The difference between the mixed flow and the ego-motion flow yields residual target motion flow where the intact target is segmented from. To estimate the ego-motion flow, a visual odometer has been implemented. We first extract some feature points in the ground plane that are identified as static points using the height constraint and Harris algorithm. And then, 6 DOF motion parameters of the moving camera are calculated by fitting the feature points into the linear least square algorithm. The approach presented here is tested on substantial traffic videos, and the results prove the efficiency of the method.

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Lateral vehicles detection using monocular high resolution cameras on TerraMax™

Cited by 22 publications

References 2 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

A Novel Approach for On-road Vehicle Detection and Tracking

Motion detection using binocular image flow in dynamic scenes

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