An improved GSTAKF-based pedestrian tracking method for abrupt state estimation

In deep-learning-based multi-object tracking frameworks, deep neural networks are used for object detection, appearance feature extraction, and data association, while filtering models perform target-state prediction and observation update for continuous trajectory estimation. Under occlusion, overlap, and false detections, matched detection boxes may exhibit position shifts and scale perturbations, causing inconsistency between current observations and prior predictions and leading to identity switches, trajectory fragmentation, and slow tracking recovery. To address these problems, this paper proposes an improved Gaussian Strong-Tracking Adaptive Kalman Filter (GSTAKF). The proposed GSTAKF jointly uses detection confidence, filtering residuals, and the strong-tracking factor to adaptively regulate the observation-noise covariance and state-update strength, thereby improving the response to low-reliability observations and abrupt motion variations. After appropriate framework-specific adaptation of the state representation, covariance scales, and filtering parameters, GSTAKF can be integrated into deep-learning-based SORT-type multi-object tracking algorithms as a modular plug-and-play motion-estimation component. Experiments on MOT17 show that under position perturbations of 0–25 pixels and scale perturbations of 0–40%, GSTAKF improves HOTA by 18.725, 17.096, 7.875, and 1.805 points when integrated into DeepSORT, StrongSORT, BoT-SORT, and HybridSORT, respectively. On MOT20, under the same perturbation ranges, GSTAKF improves HOTA from 49.022 with Standard KF to 67.328, corresponding to a gain of 18.306 points. On DanceTrack, without artificial noise, GSTAKF improves HOTA from 43.369 to 47.173, corresponding to a gain of 3.804 points over Standard KF.