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Aiming at the crowd in high-definition video motion state of sudden changes in rapid detection of abnormal crowd behavior problem, this paper proposes a kind of abnormal behavior crowd detection method based on motion vector. This algorithm is established upon the Social Force Model, first, extracts the motion vector in the code stream of the high-definition compressed videos, computes the interaction force in the social force model and rapidly draws the characteristics of the moving crowd; then according to the algorithm, we perform the bag of words approach and histogram statistics on the intensity and angle of the interaction force flow; finally we analyze two histograms to distinguish the moving state of the crowd and fulfill the detection of the abnormal crowd movement. The simulation experiment shows the method compared with the traditional social force model, in the 1024 × 768HD video frame processing speed on the average increase of 30% in average, the discrimination of abnormal frame advance 35 frames, the recall to an average increase of 22%.

In face verification applications, precision rate and identifying liveness are two key factors. Traditional methods usually recognize global faces and can not gain good enough results when the faces are captured from different ages, or there are some interference factors, such as facial shade, etc. Besides, the false face attack will pose a great security risk. To solve the above problems, this study examines how to achieve reliable living face verification based on Multi-CNNs (convolutional neural networks) and Bayes probability. Participants are required to make several expressions in random orders and contents, to ensure their liveness. Then, an effective component-based method is proposed for face recognition, and synthesized multiple CNNs can help reflecting intensities of different components. Eventually, the similarity between faces is calculated by integrating the results of each CNN, with the help of Bayes probability. Comparative experiments demonstrate that our algorithm outperforms traditional methods in face recognition accuracy. Moreover, our algorithm has unique preponderance in that it can verify the liveness of users, which can achieve higher security.

Detecting suspicious behavior from high definition (HD) videos is always a complex and time-consuming process. To solve that problem, a fast suspicious behavior recognition method is proposed based on motion vectors. In this paper, the data format and decoding features of HD videos are analyzed. Then, the characteristics of suspicious activities and the ways of obtaining motion vectors directly from the video stream are concluded. Besides, the motion vectors are normalized by taking the reference frames into account. The feature vectors that display the inter-frame and intra-frame information of the region of interest are extracted. Gaussian radial basis function is employed as the kernel function of the support vector machines (SVM). It also realizes the detection and classification of suspicious behavior in HD videos. Finally, an extensive set of experiments are performed and this method is compared with some of the most recent approaches in the field using publicly available datasets as well as a new annotated human action dataset including actions performed in complex scenarios.

This paper presents a new human targets detection algorithm for video surveillance scenes. We propose a new approach for estimation of the existence of human targets, which includes multiple modes. Based on the detection structure, different features of human targets can be extracted. There are mainly two folds of advantage. First, the system integrates the time filtration and proportion of reliability weight, in order to intelligently merge and reinforce the results of different kinds of sensors. Second, it uses a short time video analysis, which can detect the human face by a Hough Transform, in order to confirm human targets obtained by the first step. Furthermore, before face detection, we use the gray similarity for local region clustering from a whole video frame, by which the range of Hough Transform can be reduced. By these processes, the accuracy of human targets detection can be increased effectively, and the video surveillance system can be switched on regions where human bodies are detected. Therefore, it can save system resources, and enhance the whole efficiency.

3D finger vein representation is the premise of 3D finger vein authentication. In this paper, a data organization model called liana model is presented for 3D finger vein representation. Firstly, four strategies including caliber uniformity, bine classification, node partition and spread restriction are proposed to covert the complex structure of 3D finger vein into a simple binary tree structure. Then, the liana model is developed based on the binary tree. Finally, the elements, attributes and mathematical descriptions of the model are described in detail. The new model is helpful for 3D finger vein representation and 3D features extraction.

This article discusses a new algorithm for vein matching based on log-polar transform to address problems that occur with the changing of finger position and from differences between imaging devices for current vein matching algorithms. The new algorithm first extracts the feature area, which contains enough characteristics for image matching, depending on the structure of the finger vein ridge alignment. It then calculates the degree of similarity between the log-polar transform results of the model image feature areas and the sample image, and finally analyzes the result by the degree of similarity and the relationship of relative positions between feature areas. Experiments show that the algorithm is robust for rotating and zooming images of the finger vein.

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver’s face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver’s face can be detected rapidly and precisely.

In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.

Vehicle license plate location is the key of the vehicle plate recognition system. This paper proposes a new method that sufficiently utilizes the color feature such as fixed color collocation, color centers and distribution in the plate region. In this method, firstly, the input image is convolved with 1-D Gaussian filter, and the regions which have the structure and texture are similar to the license plate are extracted. Then improved fuzzy C-means (FCM) is used to color clustering in the RGB color space. Finally, according to the clustering center and color distribution, the plate region was chosen from the candidate regions. The experimental results of more than 250 images collected show that the method is more accurate and robust to environment variation, and the rate of location is above 98%.

Large terrain dataset visualization is very difficult inflight simulation, because the size of terrain dataset is orders of magnitude larger than the main memory of system in flight simulation. The dataset are usually stored out-ofcore. The relatively slow access speed of out-ofcore makes data retrieval the bottleneck of the terrain visualization in flight simulation. The previous works on large terrain dataset visualization focus on two aspects: simplifying scene and organizing data. They always organize the terrain dataset on the disks, then load them into main memory part by part and render them efficiently. However, organization of terrain data on disk is quite difficult because the error, the triangulation dependency and the spatial location of each vertex all need to be considered, and the frame deadlock or skip phenomenon will occur probably. This paper proposes a new method for large dataset loading and rending in flight simulation. This method presents a new concept called sub-dataset, which are the all data to render the terrain in aflame. By loading the sub-dataset into main memory and real-time render them, we can realize the large terrain dataset visualization in flight simulation. Virtually, the terrain dataset refer to the longitude and latitude (L/L) coordinates of the flight position. By means of the index of L/L information, we can find the sub-dataset we need to load and render. The new method is very simple, and it costs much less time, provides visual continuity, and obtains fast visualization and animation of large scale terrain inflight simulation.

In this paper, an algorithm for smoke region of focus (ROF) detection based on surveillance video is proposed in order to solve the problem of limited application in scenes range and imaging environment of the traditional smoke detection algorithm. The frog vision perception model is used in this algorithm. First the suspect regions are detected, and then the static and the dynamic features of the regions are chosen for the smoke ROF detection. Experimental results show that the algorithm is efficient and significant for improving the operational rate of the detection.