多分辨率LK光流联合SURF的跟踪方法

针对交通监控中运动目标形变、雾霾天气、高速、光照不均、部分遮挡等复杂情况导致Lucas-Kanade（LK）算法跟踪不稳定问题，提出基于多分辨率LK光流算法联合快速鲁棒性特征（SURF）的跟踪算法。所提算法构建图像多分辨率小波金字塔，解决传统LK算法中同一像素点帧间大尺度运动易丢失问题；同时联合SURF尺度不变特征变换算法，提取特征点进行光流跟踪，并制定自适应模板实时更新策略；在减少光流计算量的同时增强运动目标抗复杂环境的能力。实验结果表明，新方法中特征点匹配准确快速，自适应性强，在交通复杂化境中跟踪稳定。

Tracking method of multi-resolution LK optical flow combined with SURF

Aiming at the problem of tracking instability of the Lucas-Kanade (LK) algorithm for the complex situation of moving target deformation, fog and haze, high-speed, uneven illumination and partial occlusion in traffic monitoring, a tracking algorithm based on multi-resolution LK optical flow algorithm and Speed Up Robust Features (SURF) was proposed. The problem tracking failure for large-scale motion between frames of same pixel point in the traditional LK algorithm was solved by the proposed method, and the SURF scale invariant feature transformation algorithm was combined, feature points for optical flow tracking were extracted, and an adaptive template real-time update strategy was developed; the amount of optical flow calculation was reduced while enhancing the resistance ability of moving targets against complex environments. The experimental results show that the feature points matching of the new method is accurate and fast, which has strong adaptability and it is stable in the complicated traffic environment.