Although low-rank and sparse decomposition based methods have been successfully applied to the problem of moving object detection using structured sparsity-inducing norms, they are still vulnerable to significant illumination changes that arise in certain applications. We are interested in moving object detection in applications involving time-lapse image sequences for which current methods mistakenly group moving objects and illumination changes into foreground. Our method relies on the multilinear (tensor) data low-rank and sparse decomposition framework to address the weaknesses of existing methods. The key to our proposed method is to create first a set of prior maps that can characterize the changes in the image sequence due to illumination. We show that they can be detected by a k-support norm. To deal with concurrent, two types of changes, we employ two regularization terms, one for detecting moving objects and the other for accounting for illumination changes, in the tensor low-rank and sparse decomposition formulation. Through comprehensive experiments using challenging datasets, we show that our method demonstrates a remarkable ability to detect moving objects under discontinuous change in illumination, and outperforms the state-of-the-art solutions to this challenging problem.

In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity. This paper introduces a fast shadow detection method using a deep learning framework, with a time cost that is appropriate for robotic applications. In our solution, we first obtain a shadow prior map with the help of multi-class support vector machine using statistical features. Then, we use a semantic-aware patch-level Convolutional Neural Network that efficiently trains on shadow examples by combining the original image and the shadow prior map. Experiments on benchmark datasets demonstrate the proposed method significantly decreases the time complexity of shadow detection, by one or two orders of magnitude compared with state-of-the-art methods, without losing accuracy.

Low-rank and sparse representation based methods have attracted wide attention in background subtraction and moving object detection, where moving objects in the scene are modeled as pixel-wise sparse outliers. Since in real scenarios moving objects are also structurally sparse, recently researchers have attempted to extract moving objects using structured sparse outliers. Although existing methods with structured sparsity-inducing norms produce promising results, they are still vulnerable to various illumination changes that frequently occur in real environments, specifically for time-lapse image sequences where assumptions about sparsity between images such as group sparsity are not valid. In this paper, we first introduce a prior map obtained by illumination invariant representation of images. Next, we propose a low-rank and invariant sparse decomposition using the prior map to detect moving objects under significant illumination changes. Experiments on challenging benchmark datasets demonstrate the superior performance of our proposed method under complex illumination changes.

Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.

Illumination changes are a typical problem for many outdoor long-term applications such as visual place recognition. Keypoints may fail to match between images taken at the same location but different times of the day. Although recently some methods are presented for creating shadow-free image representations, all of them have the limitation in terms of dealing with night images and non-Planckian source of lighting. In this paper we present a new method for creating illumination invariant image representation using a combination of two existing methods based on natural image statistics that address the issue of illumination invariance. Unlike previous attempts at solving the problem of illumination invariant representation, the proposed method does not assume the ideal narrow-band color camera nor a calibration step for each environment. We evaluate our method on real datasets to establish its accuracy and efficiency. Experimental results show that our method outperforms competing methods for illumination invariant image representation.

In recent years, various background subtraction methods have been proposed and used in vision systems for moving object detection and tracking from moving cameras; however, most of them have difficulty in handling small and distant objects in complicated non-flat scenes. This paper presents a robust method to effectively segment moving objects from videos, captured by a camera on a moving platform. In our approach, a two-level registration is applied to estimate the effect of camera motion for motion compensation. After motion estimation and extraction of potential foreground pixels by Gaussian mixture model, noisy result is refined using component based and pixel based methods the latter of which uses the hidden markov model (HMM) for classifying pixels. Finally, foreground objects are tracked by a particle filter to exploit the temporal coherence of foreground motion and improve the detection accuracy through time. Experimental results show that our method outperforms competing methods for detecting moving objects in complex environments.