Ms. Pingping Wu Profile

Pingping Wu

at BEIJING INSTITUTE OF TECHNOLOGY

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Proceedings Article | 12 March 2021 Paper

WF_DiMP: weight-aware dual-modal feature aggregation mechanism for RGB-T tracking

Zhaodi Wang, Yan Ding, Pingping Wu, Jinbo Zhang

Proceedings Volume 11763, 1176362 (2021) https://doi.org/10.1117/12.2587295

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Visual object tracking has attracted a lot of interests due to its applications in numerous fields such as industry and security. Because the change of illumination could lead to RGB tracking failure, more and more researchers focus on RGB-T tracking methods based on fusion of visible and thermal infrared spectrums and hasten their development in recent years. In order to utilize dual-modal complementary information adaptively, we design a weight-aware dual-modal feature aggregation mechanism, and the WF DiMP algorithm for RGB-T tracking is therefore proposed in this paper. In WF DiMP, deep features of visible and thermal infrared images are extracted by ResNet50 and are leveraged to produce heterogenous response maps, from which dual-modal weights are learned adaptively. Weighted deep features are then concatenated as input of classifier and bounding box estimation module respectively in DiMP (Discriminative Model Prediction) network to obtain the final confidence map and an object bounding box. Experiments on VOT-RGBT2019 dataset are carried out. The results show that WF DiMP algorithm has higher tracking accuracy and robustness. The evaluation indexes PR, SR reach 82.1% and 56.3% respectively, which prove the effectiveness of our mechanism given in the paper.

Proceedings Article | 12 March 2021 Paper

An improved online multiple object tracking algorithm based on KFHT motion compensation model in the aerial videos

Pingping Wu, Hong Xu, Yan Ding, Zhaodi Wang, Jinbo Zhang

Proceedings Volume 11763, 1176397 (2021) https://doi.org/10.1117/12.2587667

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Due to the rotation of unmanned aerial vehicle, the position of object in the image could shift a lot which easily leads to tracking failure. To solve this problem, a motion compensation model based on Kalman Filter and Homography Transformation (KFHT) is designed in this paper to predict the position of trackers and to compensate position offset. And then an improved online multiple object tracking algorithm based on KFHT is proposed. In our algorithm, object appearance feature is extracted by residual CNN, the feature similarity and location association of objects are utilized to accomplish the object discrimination by two stage matching. To verify the effectives of the improved algorithm, experimental evaluation is carried out on the VisDrone2019 dataset by using YOLOv5 detection results and prior ground truth respectively. Results demonstrate that the algorithm given in this paper reduces the number of identity switches by 17% with YOLOv5 and by 66% with prior ground truth, and increases the tracking accuracy about 1.5% and 3.6% in MOTA respectively. The experimental results show that our algorithm based on the KFHT model is effective.

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