Target detection is an important problem in remote-sensing with crucial applications in law-enforcement, military and security surveillance, search-and-rescue operations, and air traffic control, among others. Owing to the recently increased availability of computational resources, deep-learning based methods have demonstrated state-of- the-art performance in target detection from unimodal aerial imagery. In addition, owing to the availability of remote-sensing data from various imaging modalities, such as RGB, infrared, hyper-spectral, multi-spectral, synthetic aperture radar, and lidar, researchers have focused on leveraging the complementary information offered by these various modalities. Over the past few years, deep-learning methods have demonstrated enhanced performance using multi-modal data. In this work, we propose a method for vehicle detection from multi-modal aerial imagery, by means of a modified YOLOv3 deep neural network that conducts mid-level fusion. To the best of our knowledge, the proposed mid-level fusion architecture is the first of its kind to be used for vehicle detection from multi-modal aerial imagery using a hierarchical object detection network. Our experimental studies corroborate the advantages of the proposed method.
Most commonly used classification algorithms process data in the form of vectors. At the same time, mod- ern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker.
Rank-1 L1-norm-based TUCKER2 (L1-TUCKER2) decomposition of 3-way tensors was recently solved exactly, for the first time, by Markopoulos et al.1 The exact solution to general-rank L1-TUCKER2 remains to date unknown. In this work, we present a novel approximate algorithm for general-rank L1-TUCKER2 decomposition of 3-way tensors. Our algorithm is accompanied by formal convergence and complexity analysis. Our numerical studies illustrate the sturdy corruption resistance of the proposed algorithm compared to state-of-the-art TUCKER2-decomposition counterparts such as GLRAM, HOSVD, and HOOI.
We present a novel method for robust tracking in video frame sequences via L1-Grassmann manifolds. The proposed method represents adaptively the target as a point on the Grassmann manifold, calculated by means of L1-norm Principal-Component Analysis (L1-PCA). For this purpose, an efficient algorithm for adaptive L1-PCA is presented. Our experimental studies illustrate that the presented tracking method, leveraging the outlier resistance of L1-PCA, demonstrates robustness against target occlusions and illumination variations.
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