Design and Implementation of Scale Adaptive Kernel Correlation Filtering Algorithm Based on Hls

• Published in: 2021 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) pp. 1 - 5
• Main Authors: Liu, Xinyu, Ma, Zhifeng, Xie, Min, Zhang, Jiahe, Feng, Tingyan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.08.2021
• Subjects: Correlation; Filtering algorithms; Filtering theory; FPGA; Kernel Correlation Filter; scale estimation; Signal processing; Signal processing algorithms; Target tracking; visual tracking; Visualization
• DOI: 10.1109/ICSPCC52875.2021.9564815

Moving target tracking is one of the important research subjects in the current field of computer vision, in which the correlation filter algorithms become a hotspot in theory research and application with its excellent comprehensive performance. However, the tracking frame of Kernel Correlation Filter (KCF) algorithms is fixed, and the change of target size will cause tracking drift or even target missing. This design improves it by integrating a scale filter from DSST (Discriminative Scale Space Tracking) algorithm so that it has a better Tracking effect under complex conditions such as target size change, occlusion, deformation and illumination change. At the same time, in order to balance the accuracy and real-time performance of the algorithm, the scale adaptive kernel correlation filtering algorithm is modified in this design, and an optimized design and implementation method based on high-level synthesis (HLS) is proposed to make it more suitable for FPGA.