SDAE deep learning target tracking algorithm based on particle filter

In currently-used target tracking algorithms, such as Mean Shift (MS), Kalman Filter (KF), Particle Filter (PF), and Convolutional Neural Network (CNN)-based trackers, the external environment significantly affects feature extraction accuracy and the success rate of target tracking. To address these limitations, we propose a hybrid algorithm combining a Stacked Denoising Autoencoder (SDAE) and Particle Filter (PF), which enhances feature extraction, reduces noise, and adapts to dynamic environmental conditions. Two functions employ a uniform update weight across frames to maintain consistent feature representation. Experimental results demonstrate that the proposed SDAE – PF algorithm achieves a tracking accuracy of 94.3%, a 12.5% improvement in robustness under environmental disturbances, and a 15.2% reduction in feature extraction errors compared to conventional methods. These results confirm the effectiveness of the proposed method for real-time, high-precision target tracking in complex scenarios.