Zhiquan Wang

Recognition of human activities using SVM multi-class classifier

Even great efforts have been made for decades, the recognition of human activities is still an unmature technology that attracted plenty of people in computer vision. In this paper, a system framework is presented to recognize multiple kinds of activities from videos by an SVM multi-class classifier with a binary tree architecture. The framework is composed of three functionally cascaded modules: (a) detecting and locating people by non-parameter background subtraction approach, (b) extracting various of features such as local ones from the minimum bounding boxes of human blobs in each frames and a newly defined global one, contour coding of the motion energy image (CCMEI), and (c) recognizing activities of people by SVM multi-class classifier whose structure is determined by a clustering process. The thought of hierarchical classification is introduced and multiple SVMs are aggregated to accomplish the recognition of actions. Each SVM in the multi-class classifier is trained separately to achieve its best classification performance by choosing proper features before they are aggregated. Experimental results both on a home-brewed activity data set and the public Schuldts data set show the perfect identification performance and high robustness of the system.

Locally optimum detection for Barni's multiplicative watermarking in DWT domain

With the increasing demands of copyright protection, watermarking technology has being paid more and more attention. In the design of watermarking algorithm, a good watermark detection scheme can improve the successful detection rate, which pushes more and more researchers to work on optimum detectors. In this paper, with respect to Barnis multiplicative watermarking that is based on human visual system (HVS), the optimum detection using Bayesian decision rule is firstly proposed. The decision threshold is obtained by the NP criterion. The probability density function of the transform coefficients is modeled by using the generalized Gaussian distribution. Its simplification equation is given using Gaussian distribution. It can result in a better performance than correlation detection.

Secure hash function based on neural network

Abstract A hash function is constructed based on a three-layer neural network. The three neuron-layers are used to realize data confusion, diffusion and compression, respectively, and the multi-block hash mode is presented to support the plaintext with variable length. Theoretical analysis and experimental results show that this hash function is one-way, with high key sensitivity and plaintext sensitivity, and secure against birthday attacks or meet-in-the-middle attacks. Additionally, the neural networks property makes it practical to realize in a parallel way. These properties make it a suitable choice for data signature or authentication.

A selective image encryption scheme based on JPEG2000 codec

In this paper, a novel image encryption scheme based on JPEG2000 is proposed, which encrypts some sensitive frequency subbands, bit-planes or encoding-passes selectively and partially. It is secure against such attack as known-plaintext attack or replacement attack. It is of low cost, keeps file format and compression ratio unchanged, supports direct bit-rate control, and does not degrade the original error-robustness. These properties make it suitable for real-time applications with direct bit-rate control requirement, such as web imaging, image communication, mobile or wireless multimedia, and so on.

Selective video encryption based on advanced video coding

Advanced Video Coding is recently announced and widely used, although the according protection means have not been developed thoroughly. In this paper, a selective encryption scheme is constructed on Advanced Video Coding. During AVC encoding, such sensitive data as intra-prediction mode, residue data, inter-prediction mode and motion vector are partially encrypted. This encryption scheme keeps secure against brute-force attack, replacement attack or known-plaintext attack, combines encryption process with compression process with low cost, and keeps the file format unchanged with some direct operations (such as displaying, time seeking, copying, cutting, etc.) supported. These properties make it suitable for secure video transmission.

Perceptual cryptography on SPIHT compressed images or videos

Perceptual cryptography encrypts multimedia data in the compression domain and causes some degradation to the decoded data. If customers are interested in the multimedia data, they can pay for one of higher quality. A perceptual cryptography on SPIHT encoded images or videos is presented. By confusing different numbers of wavelet coefficients and encrypting different numbers of coefficients signs, the images or videos can be degraded to different degrees. The encryption strength can be adjusted according to a certain quality factor, the technique supports direct bit-rate control, and is of low cost, which makes it suitable for multimedia applications with a real-time operating requirement, such as video conferencing, multimedia networks, mobile multimedia, and so on.

Perceptual cryptography on JPEG2000 compressed images or videos

In this paper, a perceptual cryptography combining with JPEG2000 codec is proposed, which permutes different number of bit-planes in each code block, encrypts different number of coefficients signs, and permutes code blocks in different frequency bands. Thus, the images or videos can be degraded to different degrees under the control of quality factor. Theoretical analyses and experimental results show that, its encryption strength can be adjusted according to certain quality factor, it is of low-cost, it supports direct bit-rate control, and it is not sensitive to transmission errors. These properties make it more suitable for network multimedia applications with real-time operation requirement, such as video-on-demand (VOD) system, multimedia communication, wireless or mobile multimedia, and so on.

Home environment fall detection system based on a cascaded multi-SVM classifier

Fall detection system for intelligent home care for elderly people is presented in this paper. The system includes human blob detection by non-parameter background substruction method, feature extraction from two minimum bounding boxes, and fall detection by a cascaded multi-SVM classifier. Besides falling down, other daily activities such as walk, jogging, sitting down, squatting down and immobility are also taken into consideration. A three-stage cascade of SVM classifiers is made to distinguish fall action from other activities. Each SVM classifier is first trained and tested separately to achieve its best classification performance by choosing proper features and corresponding kernel function. Then the combined classifier is trained to detect falls. A perfect correct identification rate of 98.13% on a real activity video set by experiments demonstrates the robustness and the utility of the system.

Robust Real-Time Detection of Abandoned and Removed Objects

This paper presents a robust real-time method for general detection of abandoned and removed objects from surveillance videos. The system introduces a unique combination of a new pixel-wise static region detector and a novel abandoned/removed object classifier based on color richness. In the static region detection phase, two backgrounds are constructed respectively to build foreground and stationary masks which are then used to update a static region confidence map. Static regions are thus extracted from the confidence map and further classified into abandoned or removed items by comparing color richness between the background and current frame. Our algorithm is easy to implement, robust to small repetitive motions, illumination change and can handle object occlusion. Experimental results on two public video databases which are shot in different scenarios demonstrate the robustness and practicability of the proposed method in real-time video surveillance.

A fast video encryption scheme based-on chaos

Video encryption is a suitable method to protect video data. There are some disadvantages in the algorithms proposed before. In this paper, we propose a novel fast MPEG video encryption algorithm, which encrypts run length codes with chaotic run-length encryption algorithm (CREA), encrypts the signs of motion vectors with security-enhanced chaotic stream cipher (SECSC) and distributes keys with chaotic key distributor (CKD) at the same time. Its security, compression ratio and computational complexity are analyzed in details. Experimental results show that, the algorithm has less effect on compression ratio than the algorithms confusing DCT coefficients do, does not change file format, and is of low cost. Thus, it is suitable for secure video encoding with real-time requirement.