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Aiming at the problem of simultaneous tracking of other vehicles in the process of intelligent vehicles driving on the road, a multi-object tracking method based on binary particle swarm optimization is proposed. In this paper, the state equation and measurement equation of tracking target are established based on radar data, the measurement data of multiple scanning periods of radar are correlated to form a variety of assumptions with possible orbit, and the orbit is scored by likelihood ratio to form a mixed integer linear programming problem, which is solved by discrete binary particle swarm optimization algorithm. The problem of correlation between the measured value and the target at each time is solved, and the track discrimination of the tracking target is realized. Finally, through the simulation in MATLAB, the data results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The plant leaf image is taken as the research target, and the improved particle swarm optimization algorithm is used to optimize the optimal threshold vector of maximum entropy threshold segmentation. Although the traditional maximum entropy segmentation method has many advantages, it also has problems such as slow algorithm operation and insufficient precision. The Improved Particle Swarm Optimization(IPSO) proposed in this paper improves the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, and has the characteristics of fast convergence and high precision. Aiming at the problem of blade overlap in plant leaf image segmentation, the RGB image is converted into HSV(Hue, Saturation, Value) image and the V vector is extracted, the fitness function of the maximum entropy thresholding method is optimized by IPSO algorithm. The experiment compares the maximum entropy value with the running time of the algorithm. The results show that the segmentation accuracy and convergence efficiency of the proposed method are greatly improved. It shows that the proposed method is superior to the traditional maximum entropy method. Therefore, the method can segment the plant leaf image more efficiently and has strong practicability in engineering. [ABSTRACT FROM AUTHOR]