Concept drift adaptation in video surveillance: a systematic review

Gonçalves, Vinícius P.; Silva, Lourival P.; Nunes, Fátima L. S.; Ferreira, João Eduardo; Araújo, Luciano Vieira de

doi:10.1007/s11042-023-15855-3

Cited by 1 publication

(1 citation statement)

References 134 publications

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“…Johnson et al pioneered the description of object motion using a flow vector (x, y, dx, dy) [ 31 ]. Building upon this, subsequent research incorporated more information into the trajectory, such as speed, angle, position, etc., forming higher-dimensional coordinate vector representations [ 32 , 33 ]. Trajectory representation based on curve fitting has also been applied; common practice involves using low-order curves to approximate trajectories, such as using cubic B-spline curves for approximation [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection

Xu,

Liu,

Yao

et al. 2023

Sensors

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The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.

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