Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach

Bedruz, Rhen Anjerome; Sybingco, Edwin; Quiros, Ana Riza F.; Uy, Aaron Christian; Dadios, Elmer P.

doi:10.1109/hnicem.2017.8269528

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…They used a fisheye camera based on simple feature points. The authors in [22] proposed an algorithm for detecting and tracking vehicles entering intersections in real‐time. The algorithm is based on blob analysis for the main tracking and means shift kernel tracking when a blob merging occurred.…”

Section: Related Workmentioning

confidence: 99%

Multi‐camera multiple vehicle tracking in urban intersections based on multilayer graphs

Delavarian

Marouzi

Hassanpour

et al. 2020

IET intell. transp. syst

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Section: Related Workmentioning

confidence: 99%

Multi‐camera multiple vehicle tracking in urban intersections based on multilayer graphs

Delavarian

Marouzi

Hassanpour

et al. 2020

IET intell. transp. syst

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“…Furthermore, we believe this detection range is sufficient to achieve turning in the case of automated driving, since the movement of autonomous vehicles is nearly identical to manual driving. There have been many previous studies of moving object detection at intersections through use of two-dimensional (2D) cameras [1][2][3][4][5][6][7], but it is difficult for 2D cameras to capture all moving objects in an intersection. Even so, some approaches focus on comprehensive moving vehicle detection using a single 2D camera installed high above the ground [8,9].…”

Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

2020

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Many complicated road intersections are seen while driving. In some, blind spots make it difficult for drivers or automated vehicles to discern moving objects coming from certain directions, possibly confusing drivers or autonomous vehicles wishing to cross or to turn at the intersection. To address this problem, we investigate detection and tracking of all moving objects at an intersection using a single 360degree-view camera (3DVC). Through experiments, we develop methods allowing a 3DVC to capture the entirety of a four-way intersection when installed at one corner. This paper also presents image processing algorithms for detecting and tracking moving objects at intersections by processing images from the installed 3DVC. Experiments under varied conditions demonstrate that the proposed detection algorithm has a very high detection rate. We also confirm the tracking ability for moving objects detected using the proposed algorithm. INDEX TERMS 360-degree camera (Omnidirectional camera), driver assistance, autonomous driving, moving object detection and tracking, image conversion.

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“…They used SVM (Support Vector Machine) and CNN (Convolutional Neural Network) classifiers to address occlusions in their tracking. Bedruz et al [25] proposed an algorithm for detecting and tracking vehicles at intersections in real time. The algorithm is based on blob analysis for main tracking and mean shift kernel tracking when a blob merging occurred.…”

Section: Introductionmentioning

confidence: 99%

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

Delavarian

Marouzi

Hassanpour

2020

IJE

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Visual vehicle tracking is an important topic in intelligent transportation systems. Intersections are challenging locations for visual systems to track vehicles which are simultaneously moving in different directions. In addition, normal traffic flow may change at intersections due to accidents. Congestion, occlusion and undetermined motion flows are the nominated challenging issues of vehicle tracking at intersections. In this paper, a method for tracking multiple vehicles is proposed considering the vehicle motion directions to overcome undetermined motion flows. For this purpose, a multilayer model is presented, which assigns each motion flows to distinct layers. Moreover, we introduce different neighborhoods for various layers considering the regular motion flows in a layer. Hence, vehicles entering from the same side of intersection with the same motion direction are assigned to the same layer. Then the tracking is performed on different layers separately. In special cases such as vehicles crossing each other, misdetections or occlusion, the proposed tracking method can predict the vehicles tracks by using the stored tracking history and considering neighborhoods in that layer. Experimental results show consistency between proposed tracking method results and ground truth, also outperformance of other tracking methods in tracking vehicles crossing the intersection.

show abstract

Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach

Cited by 24 publications

References 15 publications

Multi‐camera multiple vehicle tracking in urban intersections based on multilayer graphs

Multi‐camera multiple vehicle tracking in urban intersections based on multilayer graphs

Detection and Tracking of Moving Objects at Road Intersections Using a 360-Degree Camera for Driver Assistance and Automated Driving

A Multilayer Motion Direction Based Model for Tracking Vehicles at Intersections

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