Real-time Object Tracking Based on Improved Adversarial Learning
Bowen Song, Wei Bing Lu, Weiwei Xing, Xiang Wei, Yuxiang Yang, Limin Gao · 2020
With the development of deep learning and the emergence of massive video data, object tracking has great application prospects in many fields. However, most tracking algorithms can hardly get top performance with real-time speed. In this paper, we improved tracking model based on adversarial learning and to accelerate feature extraction we proposed an efficient and accurate method. We also present a Precise ROI Pooling (PrROIPooling) based algorithm for extracting more accurate representations of targets. Furthermore, a novel regularization term is defined to ensure the similarity between the generated features and the real features. Finally, the improved objective function with modulating factors is designed to handle the problem of imbalance in the number of positive and negative samples. Extensive experiments on three datasets have demonstrated our effectiveness and achieved competitive results compared with state-of-the-art methods.