On the detection of tracks in spectrogram images

This paper proposes an active contour algorithm for spectrogram track detection. It extends upon previously published work in a number of areas, previously published internal and potential energy models are refined and theoretical motivations for these changes are offered. These refinements offer a marked improvement in detection performance, including a notable reduction in the probability of false positive detections. The result is feature extraction at signal-to-noise ratios as low as −1 dB in the frequency domain. These theoretical and experimental findings are related to existing solutions to the problem, offering a new insight into their limitations. We show, through complexity analysis, that this is achievable in real-time.

► Commonly employed continuity and curvature measures are not suitable, and increase false positive detection rates. ► A curvature measure based on geometric properties of the track accurately models various track appearances. ► Track detection methods based upon individual pixels are unreliable at low SNRs. ► The proposed active contour algorithm effectively detects tracks of varying appearances. ► The line location accuracy metric is flawed and can rate good and bad detections similarly.