Zhouyu Fu

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Similarity based vehicle trajectory clustering and anomaly detection

In this paper, we proposed a hierarchical clustering framework to classify vehicle motion trajectories in real traffic video based on their pairwise similarities. First raw trajectories are pre-processed and resampled at equal space intervals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical clustering. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented.

Semantic-Based Surveillance Video Retrieval

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene

Recognition of Pornographic Web Pages by Classifying Texts and Images

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the objects contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages

A novel approach to detecting adult images

This work presents a novel approach to recognizing adult images. To effectively detect the ROIs (region of interest) with plentiful skin information, we structure an image with regions and points. Then based on the ROIs, we obtain some reliable features for image classification. Experimental results show that our algorithm performs well in detecting objectionable images.

Mixture clustering using multidimensional histograms for skin detection

Mixture models are frequently used to fit skin color distributions in various color spaces. However, the high computational cost of the conventional EM algorithm makes it intractable for large data sets. We propose a novel algorithm for estimating the parameters of mixture models. Multidimensional histograms are incorporated into the EM framework to group neighboring datapoints and reduce the size of the data set. We adopt this method to build Gaussian mixture models of skin color and compare the performance of models with different number of components. Further experiments on synthetic data show the efficiency of our method as a general approach to data clustering.

Skin color detection using multiple cues

In this paper, we present a novel space transformation to describe the skin and non-skin attributes, and build a new non-linear skin color classifier combining the spatial and probabilistic distributions of pixels. To weaken the illumination effect on images, we introduce a new gamma correction (GC) method. Experimental results show that our approach has good performance in skin color detection.

Adaptive skin detection using multiple cues

This paper presents an adaptive approach to skin detection. First, we propose a nonlinear relationship among R, G and B components and use a closed curve to identify the skin cluster region. Then, a split machine is designed that aids the extraction of the pixels with similar low-level features from images. Finally, a nonlinear skin color classifier with an adaptive threshold is developed by analyzing the properties of the extracted pixels in the HSL, YCbCr, YUV and YIQ color spaces. Experimental results show that our proposed method works very well in skin detection.

Fast kernel SVM training via support vector identification

Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient training algorithm involves two steps. In the first step, we randomly sample the training data without replacement several times, each time a small subset of training data is sampled. Then a kernel SVM is trained on each subset, and the resulting kernel SVM models are used to identify the support vectors on the margin. In the second step, an optimization problem is solved to estimate the Lagrange multipliers corresponding to these support vectors. After obtaining the support vectors and Lagrange multipliers, we can approximate the decision function of kernel SVM. Due to the cubic complexity of standard kernel SVM training algorithm, training many kernel SVMs on small subsets of training data is much more efficient than training a single kernel SVM on the whole training data especially for large datasets. Therefore, our algorithm has better scalability than kernel SVM. Besides, training SVMs on each subset can be done independently, and hence our algorithm can be easily parallelized for further speedup. Since our algorithm only identifies the support vectors on the margin, it produces less number of support vectors as compared to that produced by standard kernel SVM. This makes our algorithm more efficient in prediction too. Experimental results show that our method outperforms state-of-the-art methods and achieves performance on par with the kernel SVM albeit with much improved efficiency.