Resampling in standard particle filters can lead to particle impoverishment, affecting the accuracy of tracking systems. To overcome this deficiency, an improved cuckoo search algorithm-based optimization method for particle filtering in multi-object tracking is proposed. In this method, particles are treated as host nests for cuckoo birds, simulating the behavior of cuckoo birds in locating nest positions. The algorithm consists of two stages: global search and local search, which collectively guide particles towards high likelihood regions. Furthermore, enhancements are made to the cuckoo search algorithm, introducing dynamic search step sizes and reinforcing the local search mechanism, thereby improving the convergence speed of the algorithm in global search. Additionally, the improved algorithm incorporates joint probability data association for addressing multi-maneuver object tracking problems. Two sets of experiments are conducted in one-dimensional and two-dimensional environments to compare the tracking performance of the optimized particle filtering algorithm with the standard particle filtering algorithm. The experimental results demonstrate that the algorithm proposed in this paper exhibits not only faster global convergence but also an enhanced precision in multi-object tracking. In comparison to the standard Cuckoo Search Optimized Particle Filter algorithm, it showcases a 28.5% increase in global convergence iteration speed. Furthermore, when juxtaposed against the particle filter joint probability data association and particle swarm optimization particle filter joint probability data association algorithms, it shows respective accuracy enhancements of 24.7% and 11.81% in estimation precision.