Automatic Target Detection and Tracking in FLIR Image Sequences Using Morphological Connected Operator

Abstract

In this paper, we propose a method for detecting and tracking small targets in forward looking infrared (FLIR) image sequences taken from an airborne moving platform. Firstly, we adopt the morphological connected operator to remove the undesirable clutter in the background. Secondly, the image is decomposed by morphological Haar wavelet, and the wavelet energy image is computed from the horizontal and vertical detail images, and it is fused with the scaled image. Thirdly, the targets are extracted coarse-to-fine by adaptive double thresholding. Finally, targets are modeled by intensity probabilistic density function and tracked using mean shift algorithm. The experiments performed on the AMCOM FLIR data set verify the validity and robustness of the algorithm.

Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model

“…Approaches assuming low ego-motion consider that the motion component due to the camera is not very significant in comparison with the object dynamics. Under this restriction, some recent works expect that the object maintains a spatiotemporal connectivity along the sequence [1][2][3]; that is, the image regions related to the object in consecutive frames are spatially overlapped, and then they perform the 2 EURASIP Journal on Advances in Signal Processing tracking using morphological connected operators. In cases where the hypothesis about the spatiotemporal connectivity does not hold, the most common approach is to search for the object in a bounded area centered in the location where it is expected to find the object according to its dynamics.…”