For the failure of similar multi-target tracking with monocular vision caused by influence factors such as occlusion, a multi-target tracking algorithm based on improved online Hough forest tracking framework was proposed. Based on that, the tracking problem could be formulated as a detection-based trajectories association process, and the association calculation was formulated as a Maximum A Posteriori (MAP) problem with online learning Hough forest framework. Through online multi-objective samples collection and appearance and motion information extraction, a Hough forest was constructed to associate multi-target trajectories by training for track association probability. Low-rank approximation Hankel Matrix was employed to correct the trajectories, which modified associated errors and improved the efficiency of online update of the training set. Experimental results show that the trajectory miss match ratio is significantly decreased by the proposed method, and tracking accuracy and robustness of the monocular vision are effectively improved for similar or inter-occlusion targets.

When civil aviation passengers check in, various uncertainty problems exist in the baggage tag readers' number, position and angle. To solve the problems, the Dynamic Population-Double Fitness Particle Swarm Optimization (DPDF-PSO) algorithm was proposed. Firstly, the mathematical model of baggage tag detector was established, then it was transformed into an optimization problem; secondly, the optimization problem was solved by standard Particle Swarm Optimization (PSO) algorithm; finally, the standard PSO algorithm was improved in accordance with the model features. The simulation results show that compared with standard PSO algorithm, the simulation time of the DPDF-PSO algorithm reduced by 23.6%, the objective function value increased by 3.7%. DPDF-PSO algorithm overcomes the shortage of long simulation time and troublesome problem of optimal boundary solutions existed in standard PSO algorithm. Identity information can be read quickly and accurately by readers layout at a lower cost.

A hybrid control method based on backstepping and fuzzy adaptive Proportion-Integration-Differentiation (PID) was proposed, which improved the flight stability of quadrotor aircraft in different environment. The method selected the current appropriate controller according to the Unmanned Aerial Vehicles (UAV) flight environment, large attitude angle, and large attitude angular velocity. In the case of the system undisturbed, backstepping-based control algorithm could complete trajectory tracking. In case of disturbance, fuzzy adaptive PID could greatly suppress the impact of disturbance and realize the precise control of quadrotor aircraft. The Matlab simulation analysis and practical experiments illustrate that the stability augmentation hybrid controller can effectively realize the stability.