多特征自适应融合的CamShift算法与ROS跟随小车实现

针对传统的具有色彩特征的CamShift算法可以在弱干扰场景中实现良好的跟随,但在光线明显变化或相似颜色干扰的复杂场景中跟踪性能较差的劣势,采用颜色、纹理和边缘特征进行自适应融合的方法来提高算法的抗干扰能力,并通过目标框的尺寸限定和概率密度图的修正进一步提高算法在复杂场景下跟随的稳定性.分别使用机器人操作系统(robot oper-ating system,ROS)跟随小车对优化后的改进算法进行验证,结果证明了该改进算法在光线明显变化或相似颜色干扰的复杂场景下相比于传统的算法具有更高的鲁棒性、跟随精度以及良好的跟随性能.

Multi-feature Adaptive Fusion CamShift Algorithm for ROS Robot

The traditional CamShift algorithm with color features can achieve good follow in weak interference scenes, but has the disadvantage of poor tracking performance in complex scenes with obvious light changes or similar color interference. The method of adaptive fusion of color, texture and edge features is used to improve the anti-interference ability of the algorithm, and the stability of the algorithm in complex scenes is further improved by the size limitation of the target frame and the correction of the probability density map. The ROS following car is used to verify the improved algorithm, and the results show that the improved algorithm has higher robustness, following accuracy and good following performance than the traditional algorithm in complex scenes with obvious light changes or similar color interference.