To eliminate casting shadows of moving objects, which cause difficulties in vision applications, a novel method is proposed based on Visual background extractor by altering its updating mechanism using relevant spatiotemporal information. An adaptive threshold and a spatial adjustment are also employed. Experiments on typical surveillance scenes validate this scheme.

State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

A novel shadow removal approach is proposed by using block-wise transform domain shadow detection. The approach is based on the fact that the spatial frequency distributions on normal background areas and those under casted shadows from foreground objects are the same. The proposed approach is especially useful for silhouette extraction by using the Gaussian Mixture background Model (GMM) foreground segmentation in the transform domain, because the frequency distribution has already been calculated in the foreground segmentation. The stable shadow removal is realized, due to the transform domain implementation.

Most learning-based super-resolution methods neglect the illumination problem. In this paper we propose a novel method to combine blind single-frame super-resolution and shadow removal into a single operation. Firstly, from the pattern recognition viewpoint, blur identification is considered as a classification problem. We describe three methods which are respectively based on Vector Quantization (VQ), Hidden Markov Model (HMM) and Support Vector Machines (SVM) to identify the blur parameter of the acquisition system from the compressed/uncompressed low-resolution image. Secondly, after blur identification, a super-resolution image is reconstructed by a learning-based method. In this method, Logarithmic-wavelet transform is defined for illumination-free feature extraction. Then an initial estimation is obtained based on the assumption that small patches in low-resolution space and patches in high-resolution space share a similar local manifold structure. The unknown high-resolution image is reconstructed by projecting the intermediate result into general reconstruction constraints. The proposed method simultaneously achieves blind single-frame super-resolution and image enhancement especially shadow removal. Experimental results demonstrate the effectiveness and robustness of our method.

Wall shadow removal problems differ due to configuration of walls in different propagation scenarios. Applying Weiler-Atherton polygon clipping method helps calculate illuminated regions on the building walls, but unfortunately the technique has limitation when there are many walls and the walls configuration is complex. The modified Weiler-Atherton polygon clipping method proposed can solve the problem by regarding all vertices of the subject polygon or clipping polygon, that are also intersection points as simply intersection points. In the case that a certain vertex of the subject polygon is a vertex of the clipping polygon, this vertex of the subject polygon is still considered a vertex. It is found that wall shadow removal using the proposed modified Weiler-Atherton polygon clipping method is more efficient.