Object Tracking Algorithm Based on CNN and Trainable Correlation Filters
Su-Chang Lim, Jong-Chan Kim · Journal of Korea Multimedia Society · 2023
Object tracking is considered a challenging problem due to various environmental changes contained in the video sequence. Object tracking is the use of information given in the first frame to estimate the area and trajectory of a target object in a video sequence. In this paper, we propose an algorithm for object tracking using multi-scale feature maps of Resnet-50 and correlation filters. To accommodate changes in object scale, we create an appearance model using different sized feature maps extracted from each block in the network. In order to maintain the robustness of the appearance model, it is adaptively updated according to the peak value of the response map when occlusion occurs due to obstacles. Experiments result using the OTB2015 dataset showed that the proposed algorithm achieved competitive results on several image attribution such as low resolution, scale variation, occlusion and out of view.