Small dim object tracking using frequency and spatial domain information

Abstract

Small dim target tracking is an active and important research area in image processing and pattern recognition. Recently, there has been an emphasis on the development of algorithms based on spatial domain Constant False Alarm Rate (CFAR) detection. This paper presents a novel algorithm for detecting and tracking small dim targets in Infrared (IR) image sequences with low Signal to Noise Ratio (SNR) based on the frequency and spatial domain information. Using a Dual-Tree Complex Wavelet Transform (DT-CWT), a CFAR detector is applied in the frequency domain to find potential positions of objects in a frame. Following this step, a Support Vector Machine (SVM) classification is applied to accept or reject each potential point based on the spatial domain information of the frame. The combination of the frequency and spatial domain information demonstrates the high efficiency and accuracy of the proposed method which is supported by the experimental results.