Songxiao Cao

Robust visual tracking via CAMShift and structural local sparse appearance model

A new background analysis method is proposed based on sparse representation.We propose a semi-global weighted search algorithm for the local optimization problem.We develop a model update strategy for long-term robust tracking. This paper addresses issues in visual tracking where videos contain object intersections, pose changes, occlusions, illumination changes, motion blur, and similar color distributed background. We apply the structural local sparse representation method to analyze the background region around the target. After that, we reduce the probability of prominent features in the background and add new information to the target model. In addition, a weighted search method is proposed to search the best candidate target region. To a certain extent, the weighted search method solves the local optimization problem. The proposed scheme, designed to track single human through complex scenarios from videos, has been tested on some video sequences. Several existing tracking methods are applied to the same videos and the corresponding results are compared. Experimental results show that the proposed tracking scheme demonstrates a very promising performance in terms of robustness to occlusions, appearance changes, and similar color distributed background.

Attention shift-based multiple saliency object segmentation

Abstract. Object segmentation is an important but highly challenging problem in computer vision and image processing. An attention shift-based multiple saliency object segmentation model, called ASMSO, is introduced. The proposed ASMSO could produce a pool of potential object regions for each saliency object and be applicable to multiple saliency object segmentation. The potential object regions are produced by combing the methods of gPb-owt-ucm and min-cut graph, whereas the saliency objects are detected by a visual attention model with an attention shift mechanism. In order to deal with various scenes, the model attention shift-based multiple saliency object segmentation (ASMSO) contains different features which include not only traditional features, such as color, uniform, and texture, but also a new position feature originating from proximity of Gestalt theory. Experiments on the training set of PASCAL VOC2012 segmentation dataset not only show that traditional color feature and the proposed position feature work much better than features of texture and uniformity, but also prove that ASMSO is suitable for multiple object segmentation. In addition, experiments on a traditional saliency dataset show that ASMSO could also be applied to traditional saliency object segmentation and performs much better than the state-of-the-art method.

Optimal bi-directional seam carving for compressibility-aware image retargeting

Abstract A novel compressibility-aware image retargeting method based on seam carving is introduced in this paper. We propose a new significance detection method, with both the edge information and visual saliency taken into consideration. A wall-seam model is constructed to evaluate the image compressibility and assign the right number of seams for each direction. By repeatedly carving out or inserting seams we can retarget the image to a new size while preserving the important content. Finally, our algorithm is completed with the supplement of uniformly scaling, the stretched image is resized to the target size with the least structure distortion brought. Experimental results on images show that those improvements are effective and our approach can preserve image content better compared to several state-of-the-art image retargeting methods.

Video stabilisation based on modelling of motion imaging

This study proposes a novel digital video stabilisation scheme based on modelling of motion imaging (MI). The modelling of MI eliminates the speed motion as a result of a moving car, which is ignored in other models such as rotation + translation model, and estimates movement parameters of the background in video sequences captured from cameras mounted on moving cars. The authors first analyse the MI to understand the principle of the effects of car motion on MI, and select the matching method according to the proposed model. Then, they employ symmetric points to remove the speed motion. Finally, unwanted motion vector is stimulated by employing adaptive step-length filter, and the boundary compensating approach is employed to suppress the image jitter effectively. Their major contribution is the elimination of the effect of carriers speed in motion estimation. Other contributions include new robust block matching approach and adaptive-step selection for motion filtering. They conduct experiments on real videos and artificial data. Experiments on real videos show that the proposed model can remove the effect of car motion, whereas the experiments on artificial data are conducted for theoretical analysis.

Random walks colour histogram modification for human tracking

Accurate human tracking in surveillance scenes is one of the preliminary requirements for other tasks. However, when the human target is small, the extracted features may not be prominent and thus the tracking performance is unsatisfactory. The colour feature is relatively robust to the change of target size and shape, but it is prone to be affected by the background information. For the above reasons, the authors introduce random walker segmentation into human tracking and determine the background region according to the distribution characteristics of segmentation results. Even if the colour of the target is very similar to that of the background, this algorithm can segment the target. Furthermore, the principal component analysis method is used to distinguish human targets from the background as well. During tracking, the authors prevent the degradation of the target model by adding new target information. In order to overcome the mean-shift local optimisation problem, the authors search for the candidate target region with the largest weight according to the sum of all probability in each region. Experimental results further show that the authors’ tracking algorithm demonstrates better performance on tracking small human target under some challenging scenes compared with several existing tracking methods.

Ensemble Simplified Fuzzy ARTMAP with Modified Plurality Voting

In this paper, a method estimating the probability which is used in plurality voting is employed to work for plurality voting. The unknown probability in plurality voting leads to poor performance when classification is performed. Confusion matrix could estimate the probability effectively which is employed in this paper to estimate the probability. UCI datasets have been used to evaluate the applicability and performance of the proposed method. The experiments demonstrate the potential of the proposed method if offering an optimal solution to the plurality voting.