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.

To reduce the calculation complexity of the Joint Probabilistic Data Association (JPDA) joint-association events, due to multiple targets' tracks aggregation, an improved JPDA algorithm, clustering by Meanshift algorithm and optimizing confirmation matrix by Bhattacharya coefficients,was proposed.The clustering center was created by Meanshift algorithm. Then the tracking gate was obtained by calculating Mahalanobis distance between the clustering center and targets' prediction observation. The Bhattacharya likelihood matrix which was as a basis for low probability events was created, consequently the computing complexity of JPDA joint-association events which was related to low probability events was reduced. The experimental results show that the new method is superior to the conventional JPDA both in computational complexity and precision of estimation for multiple targets' tracks aggregation.