2017/08/11 by Abdullah Hamdi, Bernard Ghanem, Hamdi, Abdullah +1
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4.8 #Infrared Target Detection Methodologies #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1708.03698
openalex publication_date 2017/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.