基于fast-LOF与光流轨迹的弱小目标检测算法

An Algorithm for Weak and Small Target Detection Based on Fast-LOF and Optical Flow Trajectory

研究动态背景中弱小运动目标检测问题,提出了一种基于fast-LOF的光流轨迹分类方法。针对弱小运动目标占据像素少、特征缺失等问题,引入光流轨迹思想,在高维空间检测异常光流轨迹实现动态背景中弱小运动目标检测;针对传统LOF算法复杂度过高问题,引入fast-LOF降低异常检测环节复杂度,保证系统良好的检测效率。以手持摄像机拍摄视频进行实验,实验结果表明,白光场景中算法可以实现复杂大视场中弱小运动目标快速检测,光流轨迹和fast-LOF的结合有效提高了算法性能和检测效率,在视觉检测系统中具备一定使用价值。

In order to address the issue of detection for weak and small moving targets in dynamic background a classification method was proposed based on fast-LOF and optical flow trajectory. Weak and small moving objects only occupy a few pixels and are lack of features. To solve the problem an idea of optical flow trajectory was introduced and the abnormal optical flow trajectories were detected in highdimensional space so as to realize the detection of weak and small moving targets in dynamic background. To solve the problem of high complexity of the traditional LOF algorithm,fast-LOF was introduced to reduce the complexity of the anomaly detection link and ensure a good detection efficiency of the system. Experiments were carried out using videos captured by handheld cameras. The results showed that the algorithm can achieve rapid detection of weak and small moving targets with complex large field-of-view in white-light scenes. The combination of optical flow trajectory with fast-LOF effectively improved the performance and detection efficiency of the algorithm which has certain value in visual detection systems.