Wang Baofeng, Qi Zhiquan, Chen Sizhong, Liu Zhaodu, Ma Guocheng
Laboratory of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
PLoS One. 2017 Mar 15;12(3):e0173424. doi: 10.1371/journal.pone.0173424. eCollection 2017.
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
基于视觉的车辆检测是先进驾驶辅助系统的一个重要问题。在本文中，我们提出了一种改进的多车辆检测与跟踪方法，该方法使用级联Adaboost和具有目标身份感知的自适应卡尔曼滤波器（AKF）。构建了一个使用类Haar特征的级联Adaboost分类器用于车辆检测，随后是一个更全面的验证过程，该过程可以在位置和尺寸方面细化车辆假设。在车辆跟踪中，通过自适应卡尔曼滤波器与数据关联方法协作，以独立身份跟踪每辆车。AKF通过在线随机建模自适应地调整测量和过程噪声协方差，以补偿动态变化。数据关联使用全局最近邻（GNN）算法在考虑局部验证的同时，将不同的检测结果正确地分配给轨迹。在跟踪过程中，提出了一种基于时间上下文的轨迹管理方法，以决定是否启动、维持或终止不同对象的轨迹，从而抑制稀疏的误报并补偿临时检测失败。最后，在各种具有挑战性的真实道路上对所提出的方法进行了测试，实验结果表明，该方法的车辆检测性能有了很大提高，具有更高的准确性和鲁棒性。
