Vehicle Object Tracking Based on Fusing of Deep learning and Re-Identification
Huynh Nhat Duy, Vo Hoai Viet · International Journal of Engineering and Manufacturing · 2024
Object tracking is a popular problem for automatic surveillance systems as well as for the research community.The requirement of an object tracking problem is to predict the output including the object position at the current frame based on the input the position of the object at the previous frame.To present the comparison and experiment of some object tracking methods based on deep learning and suggestions for improvement between them in this paper, we had taken some important steps to conduct this research.First, we find out the studies related to deep learning-based object tracking models.Secondly, we examined image and video data sets for verification purposes.Thirdly, to evaluate the results obtained from existing models, we experimented with a little work related to object tracking based on deep learning networks.Fourth, based on the implemented object tracking models, we had proposed a combination of these methods.And finally, we summarize and give the evaluations for each object tracking model from the results obtained.The results show that object tracking based on Siammask model has the highest results TO score of 0.961356383 on VOT dataset and 0.969301864 on UAV123 dataset, but the possibility of errors is also high.Although the result of the combined method has few scores those are lower than the object tracking based on Siammask model, the combined method is more stable than the object tracking based on Siammask model when TME score of 16.29691993 on VOT dataset and 10.16578548 on UAV123 dataset.The Vehicle ReIdentification method results have scores that are not too overwhelming.However, the TME score is the highest with the TME score of 11.55716097 on the VOT dataset and 4.576163526 on the UAV123 dataset.