A comparative study and simulation of object tracking algorithms - Ji, Yuanfa; Yin, Pan; Sun, Xiyan; Kamarul Hawari, Ghazali; Guo, Ning (Association for Computing Machinery, 2020)
With the development of artificial intelligence technology, the problems of object tracking are getting more and more attention. This article introduces the popular object tracking algorithms, from common problems in object tracking to the classification of algorithms: Early classic trackingalgorithms, tracking algorithms based on kernel correlation filtering, and tracking algorithms based on deep learning. Then the original version and various improved versions of each type of tracking algorithm are introduced, analyzed, and compared. Finally, we use the OTB-2013 data set to test the above 50 object tracking algorithms. The simulation experiment obtained the accuracy figure and success figure of the overall performance comparison of these 50 object tracking algorithms and then selected 11 attributes to compare the performance of various algorithms. The 11 attributes are illumination variation, out-of-plane rotation, scale variation, occlusion, deformation, motion blur, fast movement, in-plane rotation, out of view, background clutter, and low resolution. The accuracy figures and success figures of these 50 algorithms are obtained respectively under the different conditions. The experiment mainly analyzes the results through three indicators of accuracy, success rate, and tracking speed of various algorithms, and draws the following conclusions: Compared with traditional algorithms, the tracking speed of correlation filtering algorithms can reach more than 100 frames per second, while the precision of deep learning methods can reach more than 0.7. The algorithms using convolution features and multi-features fusion algorithms have more advantages in tracking accuracy than the algorithm using a single feature, but the tracking speed will also drop rapidly. Using a powerful classifier is the basis of achieving good tracking. Tracker with scale estimation module can get better performance of success figures. The updating mechanisms of the model also affect the tracking accuracy.