Abstract:

In order to solve the problem that the tracking results fall into the local minimum easily and the feature extraction process is too slow due to the utilization of deep learning, we study the robust object tracking algorithm based on the Extreme Learning Machine (ELM) and Discriminative Correlation Filter(DCF). Based on the C-COT algorithm, our method improves its feature extraction way and the optimization method for the confidence map. First, a new feature extraction model is designed by using the multi-layer ELM sparse autoencoders to extract the image features efficiently and replacing the original Convolutional Neural Network(CNN). Second, after the feature extraction model, an Online Sequential Extreme Learning Machine(OS-ELM) is used to construct the target rough location estimation model and the multi-peak detection method is used to get the predicted rough location of the target. Third, the search area of the confidence map is determined according to the preliminary target location to avoid the tracking result getting into the local minimum. Finally, the effectiveness of the proposed algorithm is tested on three visual tracking benchmarks. Experimental results show that the proposed algorithm is robust to occlusion, motion blur and similar targets and has a tracking speed of 12.9 times that of the C-COT, effectively improving the tracking accuracy and speed.

Key words: object tracking, discriminative correlation filter, extreme learning machine, feature extraction, C-COT algorithm