Sensors, Vol. 20, Pages 3780: Improving Object Tracking by Added Noise and Channel Attention

Sensors, Vol. 20, Pages 3780: Improving Object Tracking by Added Noise and Channel Attention Sensors doi: 10.3390/s20133780 Authors: Mustansar Fiaz Arif Mahmood Ki Yeol Baek Sehar Shahzad Farooq Soon Ki Jung CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the propo...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research