In target tracking

the particle filter and frame-by-frame updating of the model both have poor robustness in solving the problems of occlusion

illumination change

and self-rotation. To address these challenges

we propose a new visual object tracking method based on selective model updating without timing. Forcefully updating the model on a regular basis results in target distortion and tracking drift because such updates do not consider occlusion and other background interferences. Thus

we select a mechanism to ensure that the updated model is valid and accurate. Frame-by-frame detection can also extend the tracking time and reduce video tracking efficiency. We detect the object changes within a short period to prevent the model updates from significantly affecting real-time tracking

which is of practical significance. We detect the target changes regularly based on particle filter and use the steepest gradient descent method to determine the update time. The steepest gradient descent method can be used to determine whether or not the threshold point of background interference can be reached by comparing the pixel information errors of the target

the initial model

and the model. The model is intelligent enough to represent the target

whereas the tracking frame is closer to the ground truth than the other algorithms. The proposed model is also superior to others in terms of center position error

coverage

accuracy

success rate

and time. Thus

the problems of occlusion

illumination change

and self-rotation can be solved by updating the proposed model selectively. The proposed method demonstrates excellent robustness in various scenarios under the scale-invariant condition because the dimension is not considered.