Comparison of Infrared and Visible Imagery for Object Tracking: Toward Trackers with Superior IR Performance

The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery

Maximum likelihood estimation of parameters of superimposed signals by using tree-structured EM algorithm

As an extension to the conventional EM algorithm, the tree-structured EM (TSEM) algorithm is proposed for the maximumlikelihood (ML) estimation of parameters of superimposed signals. For the special case of superimposed signals in Gaussian noise, the IQML algorithm of Bresler and Macovski is incorporated to the M-step of the EM-based algorithms, resulting in more efficient and reliable maximization. Based on simulations, it is observed that TSEM converges significantly faster than EM, but it is more sensitive to the initial parameter estimates. The hybrid-EM algorithm, which performs a few EM iterations prior to the TSEM iterations, is proposed to capture the desired features of both the EM and TSEM algorithms. Based on simulations, it is found that the hybrid-EM algorithm has significantly more robust convergence than both the EM and TSEM algorithms. © 1998 IEEE

Vessel classification on UAVs using inertial data and IR imagery

In this study, a civilian ship dataset is constructed via images captured by an infrared camera on an unmanned flying vehicle. By using this dataset and synchronized inertial data (UAV altitude and orientation, gimbal angles), a vessel classification method is proposed. The method first calculates the ship base length in meters by using segmented ship image and inertial data. By fusing the descriptors obtained from the segmented ship images and estimated ship base length, vessel classification is performed