This paper presents a feature classification method based on vision sensor in dynamic environment. Aiming at the detected targets, a double-projection error based on orb and surf is proposed, which combines texture constraints and region constraints to achieve accurate feature classification in four different environments. For dynamic targets with different velocities, the proposed classification framework can effectively reduce the impact of large-area moving targets. The algorithm can classify static and dynamic feature objects and optimize the conversion relationship between frames only through visual sensors. The experimental results show that the proposed algorithm is superior to other algorithms in both static and dynamic environments.

中文翻译：

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基于二次重投影误差特征分类算法的立体相机行走人体检测。


提出一种动态环境下基于视觉传感器的特征分类方法。针对检测到的目标，提出一种基于orb和surf的双投影误差，结合纹理约束和区域约束，实现四种不同环境下的准确特征分类。对于不同速度的动态目标，所提出的分类框架可以有效减少大面积运动目标的影响。该算法仅通过视觉传感器即可对静态和动态特征对象进行分类并优化帧间的转换关系。实验结果表明，该算法在静态和动态环境下均优于其他算法。