Multi-feature Based Ensemble Classification and Regression Tree (ECART) for Target Tracking in Infrared Imagery

Abstract

This paper considers target tracking as a binary classification problem to label pixels as either belonging to the target or the background. We present a novel robust algorithm, the multi-feature based ensemble classification and regression tree (ECART), for target tracking in infrared imagery (IR). In the first frame, a region of interest (ROI) containing target and background is initialized manually. Based on the multiple features of pixels, the ECART is trained online to distinguish between the target and the background. In the subsequent frames, the position and size of the ROI are predicted by the position and size of the target in the previous frame, respectively. Then, the ECART is used to label pixels within the predicted ROI, giving the label map. The new position and size of the target are finally found in the label map. Experimental results indicate that the proposed algorithm is effective and robust.

An Object Tracking Algorithm Based on the "Current" Statistical Model and the Multi-Feature Fusion

“…Corner detection algorithm based on image-gray proposed by Harris and Stephen [16][17][18] . Harris has been widely used in the field of computer vision and image processing.…”

Anti-Interference Aircraft-Tracking Method in Infrared Imagery

“…For target tracking in airborne forward-looking infrared imagery (FLIR), Yilmaz et al [26,27] extended the mean-shift approach by exploiting the distribution and intensity of the local standard deviation to build a dual-kernel density estimation of the mean shift, providing a general optimization solution. However, a mean-shift-based approach cannot guarantee global optimality, and is susceptible to falling into local maxima in case of clutter or occlusion [28]. When the scale of the target does not significantly vary, tracking can be performed by exploiting morphological operators.…”