Chai Yi

Sift-based camera tamper detection for video surveillance

Keeping the camera long time proper functioning without tamper is the fundamentally requirement of a video surveillance system. Traditional camera tamper detection is applied by surveillance system operators. Its large human resource consuming and inefficiency. In this paper, a SIFT-based automatic camera tamper detection algorithm for video surveillance is proposed. When camera tamper occurred, the real-time frame will be large changed. Therefore, a Sift feature based decision function is employed to detect camera tamper. The threshold is carefully chosen to reduce false alarms. Several experiments are conducted to demonstrate the effectiveness and robust of the proposed method.

Distributed model predictive control for multi-agent systems with coupling constraints

This paper proposes an improved distributed model predictive control (DMPC) scheme for multi-agent systems with coupling constraints by applying compatibility constraint and deviation penalisation. Firstly, a sufficient condition based on the compatibility constraint to satisfying cooperative coupling constraints is given, which enables control performance and coupling constraints dependent on the compatibility constraint. Then, in order to optimise the control trajectory and to improve the consistency of the actions between agents, the deviation between what an agent is actually doing and what its neighbours believe it is doing is penalised in the cost function. At each sampling instant, the compatibility constraint of each agent is set tighter than the previous sampling instant. The optimal control problem is formulated as quadratic programming with quadratic constraints. Finally, two simulation examples are given to illustrate the effectiveness of the proposed scheme.

An Improved Toeplitz Measurement Matrix for Compressive Sensing

Compressive sensing (CS) takes advantage of the signals sparseness in some domain, allowing the entire signal to be efficiently acquired and reconstructed from relatively few measurements. A proper measurement matrix for compressive sensing is significance in above processions. In most compressive sensing frameworks, random measurement matrix is employed. However, the random measurement matrix is hard to implement by hardware. So the randomness of the measurement matrix leads to the poor performance of signal reconstruction. In this paper, Toeplitz matrix is employed and optimized as a deterministic measurement matrix. A hardware platform for signal efficient acquisition and reconstruction is built by field programmable gate arrays (FPGA). Experimental results demonstrate the proposed approach, compare with the existing state-of-the-art method, and have the highest technical feasibility, lowest computational complexity, and least amount of time consumption in the same reconstruction quality.

An Improved Mean-Shift Tracking Algorithm using PSO-Based Adaptive Feature Selection

Abstract Traditional mean-shift tracking algorithm use pre-defined tracking feature. Its trends to lead tracking failure in the complex background scenes and fast-changing background scenes. In this paper, an improved mean-shift tracking algorithm using Particle swarm optimization (PSO) based adaptive feature selection is presented to improve the tracking performance. We assume that the features with best discrimination between object and background are also the best for tracking the object. A two-class variance ratio is employed to measure the discrimination. PSO algorithm is used to optimize the different feature combination to adaptively generate the best tracking feature. Experimental results show that the proposed method can improve the performance of mean-shift tracker significantly in the complex and fast-changing background scenes.

Distributed model predictive control with communication complete failure for multi-aircrafts formation

This paper proposed a dual-mode decentralized model predictive control (DMPC) method with communication complete failure for multi-aircrafts formation. The formation problem with normal communication is formulated as decentralized model predictive control scheme based on flying state information translation and compatibility constraint. And the coupled constraint (collision avoidance) between aircrafts is taken as noncoupled constraint which is depended on compatibility constraint. The formation problem with communication complete failure is formulated as decentralized model predictive control scheme based on flying state detection and optimization of multi-aircrafts.The proposed decentralized control scheme is designed as quadratic programs with quadratic constraint. A simulation example is given to illustrate the effectiveness of the proposed scheme.

A Robust Object Tracking Algorithm Based on Surf and Kalman Filter

In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURFmatching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.In this paper, an efficient robust object tracking approach based on SURF and Kalman Filter is proposed. SURF as an outstanding local invariant feature is employed. Based on the SURF feature, a SURF match method is proposed. A combination method using an ingenious method and KF is used to predict the possible region, in which the tracking object may appear. Only in this region, SURF features are extracted and matched. It can significantly reduce the computational complexity. A histogram-based re-match process is employed to dislodge failure tracking after SURF matching. To verify the performance of the proposed algorithm, several comparative experiments are conducted. The results reveal that the proposed method achieves better performance and accuracy than conventional methods.

Distributed model predictive control of the multi-agent systems with improving control performance

This paper addresses a distributed model predictive control (DMPC) scheme for multiagent systems with improving control performance. In order to penalize the deviation of the computed state trajectory from the assumed state trajectory, the deviation punishment is involved in the local cost function of each agent. The closed-loop stability is guaranteed with a large weight for deviation punishment. However, this large weight leads to much loss of control performance. Hence, the time-varying compatibility constraints of each agent are designed to balance the closed-loop stability and the control performance, so that the closed-loop stability is achieved with a small weight for the deviation punishment. A numerical example is given to illustrate the effectiveness of the proposed scheme.

Feature Analysis and Modeling of the Network Community Structure

Community structure has an important influence on the structural and dynamic characteristics of the complex systems. So it has attracted a large number of researchers. However, due to its complexity, the mechanism of action of the community structure is still not clear to this day. In this paper, some features of the community structure have been discussed. And a constraint model of the community has been deduced. This model is effective to identify the communities. And especially, it is effective to identify the overlapping nodes between the communities. Then a community detection algorithm, which has linear time complexity, is proposed based on this constraint model, a proposed node similarity model and the Modularity Q. Through some experiments on a series of real-world and synthetic networks, the high performances of the algorithm and the constraint model have been illustrated.

On distributed model predictive control for systems with decoupled dynamics

This paper studies distributed model predictive control for the system which is composed of several subsystems with decoupled dynamics. Couplings between different subsystems are manifested by the cost function and constraints. The couplings are described through a connected graph. In this connected graph, each code denotes a subsystem. The cost function and constraints of a code are the functions of its state and the states of its neighbors. The centralize MPC controller for the over system is broken into distinct distributed MPC controllers of smaller size. Each MPC controller is associated to a different node. The distributed MPC and centralize MPC are compared through a numerical example.

Research On The Control System of Autonomous Tunneling Robot

In this paper, using the inertial navigation technology in the tunneling machine control system designs an autonomous tunneling robot. The data of situation and location of tunneling robot can be acquired by inertial measurement system. According to work plan and moving track, and analyzing the robot situation information the robot can be controlled to track the 3D data given in advance. The autonomous tunneling robot is realized.