Abstract: Aiming at the problem of object tracking drift caused by occlusion factors and target scale changes during the tracking process,this paper proposes an adaptive scale and learning rate-adjusted background-aware correlation filter tracking algorithm.First,this algorithm obtains the target's initial position information through the background-aware correlation filter;then,it trains the scale correlation filter under the basic framework of the background-aware correlation filter and estimates the target scale change effectively,thus accurately adjusting the search area size;next,the occlusion determination is performed according to the fluctuation of the response map,and the average peak energy index and the maximum response value are used to estimate target occlusion,thus enabling the model to adaptively adjust learning rate;finally,this algorithm designs the corresponding model update strategy to improve the model performance.This algorithm is tested on the OTB100 Benchmark dataset,and test result show that this algorithm improves the success rate by 6.2% and the accuracy by 10.1% compared with the background-aware correlation filter.Therefore,the proposed algorithm can effectively deal with occlusion and scale changes,improve the success rate and accuracy of the tracking model,and have a real-time tracking speed.

Key words: Correlation filter, Learning rate, Model update, Object tracking, Scale estimation