This paper proposes a new enhanced method based on one-dimensional direct linear transformation for estimating vehicle movement states in video sequences. The proposed method utilizes a contoured structure of target vehicles, and the data collection procedure is found to be relatively stable and effective, providing a better applicability. The movements of vehicles in the video are captured by active calibration regions while the spatial consistency between the vehicle’s driving track and the calibration information are in sync. The vehicle movement states in the verification phase are estimated using the proposed method first, and then the estimated states are compared with the actual movement states recorded in the experimental test. The results show that, in the case of camera perspective of 90 degrees, in all driving states of low speed, high speed, or deceleration, the error between estimated speed and recorded speed is less than 1.5%, the error of accelerations is less than 7%, and the error of distances is less than 2%; similarly, in the case of camera perspective of 30 degrees, the errors of speeds, distances, and accelerations are less than 4%, 5%, and 10%, respectively. It is found that the proposed method is superior to other existing methods.

中文翻译：

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基于视频的车辆运动重构的计算方法

本文提出了一种基于一维直接线性变换的增强方法，用于估计视频序列中的车辆运动状态。所提出的方法利用目标车辆的轮廓结构，并且数据收集过程被发现是相对稳定和有效的，提供了更好的适用性。视频中车辆的运动由有效的校准区域捕获，而车辆的行驶轨迹与校准信息之间的空间一致性则保持同步。首先使用提出的方法估算验证阶段的车辆运动状态，然后将估算的状态与实验测试中记录的实际运动状态进行比较。结果表明，在摄像机视角为90度的情况下，在所有低速行驶状态下，高速或减速时，估算速度与记录速度之间的误差小于1.5％，加速度误差小于7％，距离误差小于2％；类似地，在摄像机视角为30度的情况下，速度，距离和加速度的误差分别小于4％，5％和10％。发现所提出的方法优于其他现有方法。