Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.

中文翻译：

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使用自适应地平面估计和约束多核的基于移动相机的目标跟踪

基于移动相机的智能交通系统（ITS）目标跟踪方法越来越受到关注。然而，驾驶环境的不可预测性和来自相机校准的噪声使得传统的地平面估计不可靠，并对跟踪结果产生不利影响。在本文中，我们提出了一种使用自适应地平面估计算法的对象跟踪系统，通过约束多核 (CMK) 跟踪和卡尔曼滤波促进，以不断更新移动对象的位置。该算法利用运动结构（SfM）来估计运动相机的位姿，然后将估计的相机偏航角作为反馈，提高地平面估计的精度。为了进一步稳健有效地跟踪遮挡下的对象，所提出的系统采用受约束的多核跟踪技术来跟踪 3D 空间（深度）中的移动对象。所提出的系统在几个具有挑战性的数据集上进行了评估，实验结果显示出良好的性能，它不仅可以有效地跟踪配备在自由移动车辆上的行车记录仪中的道路物体，而且可以很好地处理跟踪中的遮挡。