Jianbin Xie

A fast recognition algorithm for suspicious behavior in high definition videos

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.

A Fast and Robust Algorithm for Fighting Behavior Detection Based on Motion Vectors

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.

Finger-vein pattern restoration with Direction-Variance-Boundary Constraint Search

Finger-vein verification is an emerging biometrics technology. Its first task is extracting finger-vein patterns. Although existing algorithms can extract most finger-vein patterns robustly, some branch of these patterns always breaks, which leads to adverse effects for features extraction and matching. In this paper, a Direction-Variance-Boundary Constraint Search (DVBCS) model is presented to restore the broken finger-vein patterns. At the beginning, endpoints of broken finger-vein branches are located. Then, a direction constraint for searching candidate point set is demonstrated. Following the second stage, an optimal target point is selected from the candidate point set according to a minimum within-cluster variance criterion. Eventually, the boundary constraint and variance constraint are introduced as the termination conditions. Experimental results illustrate that, while maintaining low segmentation error, the proposed method can restore above 10% lost target points. Moreover, the equal error rate of finger-vein recognition is reduced from 0.57% to 0.29% when using the proposed method to restore finger-vein patterns.

Finger Vein Representation by Modified Binary Tree Model

Finger vein recognition has high identification accuracy and strong security performance, which can be used in banks, offices, factories, etc. Although image representation is not a necessary process for finger vein recognition, a proper representation method can help to explore distribution regularities and structure differences of finger veins, and provides instructive information for finger vein recognition. It is very difficult to represent finger veins because of their irregular structure. Therefore, four principles (caliber uniformity, node replication, loop splitting, and virtual connection) are proposed in this paper, first to simplify the finger vein structure as a binary tree structure. Then a modified binary tree model is proposed based on the binary tree structure. The new model uses the binary tree to describe the relationships between different vein branches and uses a B-spline function to describe the spatial structure of vein branches. Experiments show that this model can quantitatively describe the relationships between, and the spatial structure of, vein branches with little representation error and low storage space requirements.

Driver’s Face Detection Using Space-time Restrained Adaboost Method

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.

Two-dimensional complete neighborhood preserving embedding

Complete neighborhood preserving embedding (CNPE) is an improvement to the neighborhood preserving embedding (NPE) algorithm, which can address the singularity and stability problems of NPE and at the same time preserve useful discriminative information. However, CNPE works with vectorized representations of data, and thus, the original 2D face image matrices should be previously transformed into the same dimensional vectors. Such a matrix-to-vector transform usually leads to a high-dimensional image vector space, which makes the eigenanalysis quite difficult and time-consuming. Beyond computational issues, some spatial structural information between nearby pixels may be lost after vectorization. In this paper, we develop a new scheme for image feature extraction, namely, two-dimensional complete neighborhood preserving embedding (2D-CNPE). 2D-CNPE builds the eigenmatrix and the weight matrix which characterize local neighborhood properties of data directly based on the original face images, and then, the optimal embedding axes are obtained by performing an eigen-decomposition. Experimental results on three face databases show that the proposed 2D-CNPE achieves better performance than other feature extraction methods, such as Eigenfaces, Fisherfaces, and 2D-PCA.

An Algorithm for Vein Matching Based on Log-polar Transform

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.

AN ALGORITHM FOR SMOKE ROF DETECTION BASED ON SURVEILLANCE VIDEO

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 enviro...

Joint Template Matching Algorithm for Associated Multi-object Detection

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

Robust Recognition Algorithm for Fall Down Behavior

Detecting fall down behavior is a meaningful work in the area of public video surveillance and smart home care, as this behavior is often caused by accident but usually trigger serious result. However, the uncertain individual behavior, the difference between different cameras, and the complexity of real application scene make the work absolutely hard. In this paper, a robust fall down behavior recognition algorithm is proposed based on the spatial and temporal analysis of the Key Area of Human Body (KAHB). Firstly, a modified ViBe method is applied to extract motion area. Then a pre-trained human body classifier combined with histogram tracking is used to locate the KAHB and extract its normalized spatial and temporal features. Finally, a SVM classifier is employed to find the fall down behavior.