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Background subtraction is the fundamental step of moving object detection. As current methods use specific data-based or data learning methods to represent background, this will induce the problem of model distortion while the scenarios cannot meet their assumptions or model conditions. A novel data-driven framework is proposed to represent the background using the intrinsic characteristics of the background. In the framework, the model-free adaptive control method is used as an instance to analyze the background by its status of the nearest time instants, and linearize them dynamically from a pure data perspective. To overcome the occlusion of foreground objects, the selective update method is employed to satisfy the background update. Experiments are carried out under different video conditions to compare algorithm performance with state-of-the-art background models. The results show that the proposed method has reached over 95% in F-measure and percentage of correct classification in most cases, which is better than other state-of-the-art methods. Furthermore, the proposed method shows better robustness in severe video conditions, including bad weather and night cases, and its simplified data-driven control laws make it suitable for outdoor video surveillance.

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The increase of e-bikes has raised traffic conflict concerns over past decade. Numerous conflict indicators are applied to measure traffic conflicts by detecting differences in temporal or spatial proximity between users. However, for traffic environment with plenty of e-bikes, these separate space-time approaching indicators may not be applicable. Thus, this study aims to propose a multi-variable conflict indicator and build a conflict identification method for e-bikes moving in the same direction. In particular, by analysing the conflict characteristics from e-bikes trajectories, a multi-variable conflict indicator utilizing change of forecast post encroachment time, change of relative speed and change of distance is derived. Mathematical statistics and cluster discriminant analyses are applied to identify types of conflict, including conflict existence identification and conflict severity identification. The experimental results show: in mixed traffic environments with many e-bikes, compared with time-to-collision and deceleration, accuracy of identifying e-bike conflict types based on proposed method is the highest and can reach more than 90%; that is, multi-variable indicator based on time and space are more suitable for identifying e-bike conflicts than separate space-time approaching indicators. Furthermore, setting of dividing strip between motor vehicle and non-motorized vehicle has significant influence on number and change trend of conflict types. The proposed method can not only provide a theoretical basis and technical support for automated conflict detection in mixed transportation, but also give the safety optimization sequence of e-bikes at different types of intersections.

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