Tracking multiple objects in urban traffic environments using dense stereo and optical flow

Bota, Silviu; Nedevschi, Sergiu

doi:10.1109/itsc.2011.6082960

Cited by 16 publications

(18 citation statements)

References 14 publications

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“…In [133], the concept of 6D vision, i.e., the tracking of interest points in 3-D using Kalman filtering, along with ego-motion compensation, is used to identify moving and static objects in the scene. Optical flow is also used as a fundamental component of stereo-vision analysis of the on-road scene in [84], [117], [126], [127], [131], and [133]- [137]. A 3-D version of optical flow, in which a least squares solution to 3-D points' motion is solved, is used in [138].…”

Section: ) Motion-based Approachesmentioning

confidence: 99%

“…In [5] and [126], vehicles are detected in the monocular plane using an AdaBoost-based classification and tracked in 3-D using Kalman filtering in the stereo domain. In [137], vehicles' positions and velocities are estimated using Kalman filtering. In [147], Kalman filtering is used to track objects detected by clustering, stereo matching linear cameras.…”

Section: B Stereo-vision Vehicle Trackingmentioning

confidence: 99%

“…This is to say that the typical paradigm consists of ego-motion compensation, tracking feature points in 3-D, distinguishing static from moving points, and associating moving points into moving objects [84]. Finally, moving objects are labeled as vehicles by fitting a cuboid model [137] or clustering [130]. While these methods have made great progress, complex scenes still present difficulty [85].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

See 2 more Smart Citations

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.

811

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: ) Motion-based Approachesmentioning

confidence: 99%

Section: B Stereo-vision Vehicle Trackingmentioning

confidence: 99%

Section: A Vehicle Detectionmentioning

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.

811

322

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show abstract

“…The feature points with optical flow and disparity flow not consistent with the estimated ego-motion indicate the existence of independently moving objects. In [18], Bota and Nedevschi focus on fusing stereo and optical flow for multi-class object tracking by designing Kalman filter fitted with static and dynamic cuboidal object models. In [19], interest moving points are first detected and projected on 3D reconstruction ground plane using optical flow and stereo disparity.…”

Section: Introductionmentioning

confidence: 99%

“…Several groups have reported stereo-based ego-motion estimation based on tracking point features. In [18], the concept of 6D vision, i.e., the tracking of interest points in 3D using Kalman filtering, along with ego-motion compensation, was used to identify moving objects in the scene. In [21], vehicle's ego-motion was estimated from computational expensive dense stereo and dense optical flow with the method of iterative learning from all points in the image.…”

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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Equivalence of Correlation Filter and Convolution Filter in Visual Tracking

Zhao

Feng

et al. 2021

Lecture Notes in Computer Science

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Tracking multiple objects in urban traffic environments using dense stereo and optical flow

Cited by 16 publications

References 14 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

Motion detection using binocular image flow in dynamic scenes

Equivalence of Correlation Filter and Convolution Filter in Visual Tracking

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