Shuxiao Li

Adaptive pyramid mean shift for global real-time visual tracking

Tracking objects in videos using the mean shift technique has attracted considerable attention. In this work, a novel approach for global target tracking based on mean shift technique is proposed. The proposed method represents the model and the candidate in terms of background weighted histogram and color weighted histogram, respectively, which can obtain precise object size adaptively with low computational complexity. To track targets whose displacements between two successive frames are relatively large, we implement the mean shift procedure via a coarse-to-fine way for global maximum seeking. This procedure is termed as adaptive pyramid mean shift, because it uses the pyramid analysis technique and can determine the pyramid level adaptively to decrease the number of iterations required to achieve convergence. Experimental results on various tracking videos and its application to a tracking and pointing subsystem show that the proposed method can successfully cope with different situations such as camera motion, camera vibration, camera zoom and focus, high-speed moving object tracking, partial occlusions, target scale variations, etc.

Fast curvilinear structure extraction and delineation using density estimation

Detection and delineation of lines is important for many applications. However, most of the existing algorithms have the shortcoming of high computational cost and can not meet the on-board real-time processing requirement. This paper presents a novel method for curvilinear structure extraction and delineation by using kernel-based density estimation. The method is based on efficient calculation of pixel-wise density estimation for an input feature image, which is termed as local weighted features (LWF). For gray and binary images, the LWF can be efficiently calculated by integral image and accumulated image, respectively. Detectors for small objects and centerlines based on LWF are developed and the selection of density estimation kernels is also illustrated. The algorithm is very fast and achieves 50 fps on a PIV2.4G processor. Evaluation results on a number of images and videos are given to demonstrate the satisfactory performances of the proposed method with its high stability and adaptability.

Vision-aided UAV navigation using GIS data

This paper proposes a novel vision-aided navigation architecture to aid the inertial navigation system (INS) for accurate unmanned aerial vehicle (UAV) localization. Unlike previous image localization methods such as scene matching and terrain contour matching, our approach registers meaningful object-level features extracted from real-time aerial imagery with the data of geographic information system (GIS). Firstly, we extract from aerial images the widely distributed object features including roads, rivers, road intersections, villages, bridges et al.. Then, the extracted image features are delineated as geometrical points and vectors, which coincide with the representation of GIS data. Finally, GIS model is constructed by corresponding geographical object information from GIS data, and visual geometrical features are registered with GIS model to obtain the absolute position of the image. The proposed method adopts GIS as reference data, thus the storage requirement is lower than that of scene matching. In addition, all steps of this approach can be calculated efficiently, while the computational cost of terrain contour matching is very high. Simulation results demonstrate the feasibility of the proposed method for UAV localization.

Visual object tracking using spatial Context Information and Global tracking skills

Tracking objects in videos by the mean shift algorithm with color weighted histograms has received much attention in recent years. However, the stability of weights in mean shift still needs to be improved especially under low-contrast scenes with complex motions. This paper presents a new type of color cue, which produces stable weights for mean shift tracking and can be computed pixel by pixel efficiently. The proposed color cue employs global tracking techniques to overcome the illustrated drawbacks of the mean shift algorithm. It represents a target candidate with a larger scale than that of the target model so that the model is much more precise than the candidate. We illustrate that the weights by this way are more reliable under various scenes. To further suppress surrounding clutters, we establish a new spatial context model so that the optimization results are a set of weights which can be computed pixel by pixel. The proposed color cue is called CIG since it computes the weights based on spatial Context Information and Global tracking skills. Experimental results on various tracking videos show that weight images by CIG have higher stability and precision than those of current methods especially under low-contrast scenes with complex motions

Matching Road Networks Extracted from Aerial Images to GIS Data

In aerial images, road network is the most salient artificial object, and could provide lots of geographical information. Thus it is very valuable for navigation systems,e.g. cruise missile or UAV(unmanned Aerial Vehicle) navigation system. With the presence of GIS in which road network is also the most common data, the position of aerial image can be located by matching it with a model generated from GIS. There are many algorithms presented to match images with model, and mostly the computational costs of them are very expensive, which can not meet the on-line processing requirement. In this paper, we first represent the GIS data as a model(GIS model in abbreviation in the following text).Then, a method for registering the road network picked up from the aerial images with GIS model is depicted.Several aerial images are taken to test the effectiveness of our method. Experimental results demonstrated that the correctness and veracity are both satisfying, and the cost of our method is acceptable.

Recognizing and Filtering Web Images Based on People's Existence

Judging whether a Web image contains people is useful in both pornographic image recognition and image filtering when searching for images of people. We proposed an approximate but rapid method to solve this problem. For a Web image, three types of probabilities are calculated from the image itself, the imagepsilas associated texts and the title of the Web page is located, respectively. Then a final probability representing the peoplepsilas existence is achieved by fusion of the three probabilistic values. Based on the probability of peoplepsilas existence, we proposed a two-layer framework for pornographic image recognition and a solution of image retrieval respectively. In the experiments conducted, our proposed framework and solution demonstrate good performances in the image recognition and filtering respectively.

Evaluation of Feature Detectors and Descriptors for Motion Detection from Aerial Videos

In tasks of motion detection from aerial videos, feature-based image registration is an essential step to compensate ego motion of airborne vehicle between consecutive frames. This paper presents the first performance evaluation of feature detectors and descriptors for image alignment and frame difference to detect pixels with motion from aerial videos. To this end, we design two criteria, namely Position Error Rate(PER) and Correct Match Rate(CMR), to characterize the registration accuracy and frame difference success rate, respectively. To generate the pixel-wise registration ground-truth, we employ sophisticated block-matching method, which is then checked and corrected manually by control-points-based alignment method. Based on the proposed metrics and ground-truth registration parameters, five detectors (Harris, FAST, SUSAN, DoG, and SUSAN_M) and four descriptors (Intensity, BRIEF, HOG and SIFT) are examined. We test detector-descriptor combinations in typical visual light aerial videos and infrared aerial videos. We find that detector plays a more important role in both registration accuracy and efficiency than descriptor does, thus should receive more attention in the area of motion detection from aerial videos. For detectors, DoG performs well in most videos but has the lowest efficiency, and SUSAN_M achieves good performance balance between registration accuracy and efficiency. We also reveal that currently widely used detectors should be tailored to moving object detection tasks in future research on the aspects of feature spatial layout, removing features on moving targets, feature number control, as well as computational efficiency.

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves four steps. Firstly, both the motion history image and the tracking trajectory are employed to extract candidate target regions. Secondly, the spatial-temporal saliency is used to detect moving objects in the candidate regions. Thirdly, the previous detected objects are tracked by mean shift in the current frame. And finally, the detection results are fused with the tracking results to get refined detection results, in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

Specific changes detection in visible-band VHR images using classification likelihood space

Object-based post-classification change detection methods are effective for very high resolution images, but their effectiveness is limited by incomplete class hierarchy and complex image object comparison. In this paper, a novel Classification Likelihood Space (CLS) is proposed to synthesize the effective object-based image analysis and easy-to-implement post-classification comparison, serving as a well tradeoff between performance and complexity. The proposed algorithm is tested on a dataset which comprises 102 pairs of visible-band very high resolution real satellite images, and a great improvement is observed over traditional post-classification comparison.

Corner detector using invariant analysis

Corner detection has been shown to be very useful in many computer vision applications. Some valid approaches have been proposed, but few of them are accurate, efficient and suitable for complex applications (such as DSP). In this paper, a corner detector using invariant analysis is proposed. The new detector assumes an ideal corner of a gray level image should have a good corner structure which has an annulus mask. An invariant function was put forward, and the value of which for the ideal corner is a constant value. Then, we could verify the candidate corners by compare their invariant function value with the constant value. Experiments have shown that the new corner detector is accurate and efficient and could be used in some complex applications because of its simple calculation.