Experimental evaluation of feature channels for object tracking in RGB and thermal imagery using correlation filter

Correlation filter based trackers are well studied for object tracking and shown great interest to the research community in recent years. The vast majority of the works make utilization of either color feature channels or Histogram of Gradient feature channels for object tracking in visual spectrum. However, the strength of feature channels varies from RGB videos to thermal infrared videos. Subsequently, an assessment of feature channels in RGB and thermal imagery is needed to select the best features. In this work, we study the performance of various feature channels under kernelized correlation filter framework in RGB recordings, by taking 33 videos from object tracking benchmark (OTB) dataset and thermal infrared recordings, by taking 25 thermal videos from Thermal InfraRed (LTIR) dataset. Performance of each feature channels in both imaging modes are quantified using distance precision score, overlap score, average center location error and speed metrics. The best performance is obtained when HOG and color name features are utilized for RGB videos and gradient and gabor features are used in thermal videos among selected feature sets in kernelized correlation filter framework.