Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high detection requirements and the slow target-extraction problem of a common camera, this article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states. For a better detection work, the DVS image’s noise points are filtered and the CTRV kinematics model is built in advance. In order to distinguish the overlapped or nearby bodies and get the accurate clustering number, this article proposes a 3-D improved $K$ -means method. As the clustering centers can be influenced by the movement easily, the moving objects’ clustering centers appear unstable, so the movement estimation also fluctuates. In order to obtain stable movement estimation, this article proposes a strong tracking center differential external Kalman filter (SCDEKF) to track the moving objects, and the method has higher accuracy and less computational load. In order to verify the advantages of proposed methods, a simulation environment was established in the Gazebo, and the common cameras were also added in simulation for comparison with the DVS sensor. The simulation results show that the 3-D improved $K$ -means method with DVS can cluster the moving objects accurately, and the SCDEKF can provide more accurate movement estimation than the traditional EKF method. Finally, two experiments were conducted to prove the methods’ superiority. The main contribution is to solve the exploitation problems faced by the short-term borne sensor and promote its application in transportation.

中文翻译：

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基于神经形态视觉传感器的地面车辆检测与运动跟踪

运动对象检测是自动驾驶汽车的一项关键能力。面对普通相机的高检测要求和慢目标提取问题，本文提出利用神经形态视觉传感器（DVS）检测运动物体并估计其运动状态。为了更好地进行检测，将对DVS图像的噪声点进行滤波，并预先建立CTRV运动学模型。为了区分重叠或附近的物体并获得准确的聚类数，本文提出了一种3D改进方法 $ K $ -均值方法。由于聚类中心很容易受到运动的影响，因此运动对象的聚类中心显得不稳定，因此运动估计也会发生波动。为了获得稳定的运动估计，本文提出了一种强大的跟踪中心差分外部卡尔曼滤波器（SCDEKF）来跟踪运动对象，该方法具有较高的精度和较少的计算量。为了验证所提出方法的优点，在凉亭中建立了仿真环境，并且在仿真中还添加了通用摄像机以与DVS传感器进行比较。仿真结果表明，改进了3-D $ K $ 使用DVS的-means方法可以准确地对运动对象进行聚类，并且SCDEKF可以提供比传统EKF方法更准确的运动估计。最后，通过两个实验证明了该方法的优越性。主要的贡献是解决短期传播传感器面临的开发问题，并促进其在运输中的应用。