A multi-view object tracking using triplet model

Abstract Object tracking is very important in intelligent systems, such as video surveillance, automatic drive and traffic security. Background subtraction algorithm is a mature method for foreground object extraction, but it may be affected by complicated background or the change of object shape. So in this paper, a novel object tracking method is proposed using a triplet model. First, BING feature is used to find some potential object proposals. Then, we construct a triplet model for each potential object. The triplet of the same object between two consecutive frames is considered similar. Finally, object tracking can be achieved by computing feature difference of triplets. Experimental results show that our method can achieve object tracking effectively and in real time.

[1]  Xuelong Li,et al.  Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation , 2014, IEEE Transactions on Image Processing.

[2]  Lei Guo,et al.  When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Ling Shao,et al.  Face recognition with a small occluded training set using spatial and statistical pooling , 2018, Inf. Sci..

[4]  Dong Xu,et al.  Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey , 2018, IEEE Signal Processing Magazine.

[5]  Michael Jones,et al.  An improved deep learning architecture for person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Deyu Meng,et al.  Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Xiaogang Wang,et al.  DeepReID: Deep Filter Pairing Neural Network for Person Re-identification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Junwei Han,et al.  Duplex Metric Learning for Image Set Classification , 2018, IEEE Transactions on Image Processing.

[9]  Feiping Nie,et al.  Robust Object Co-Segmentation Using Background Prior , 2018, IEEE Transactions on Image Processing.

[10]  Feiping Nie,et al.  Revisiting Co-Saliency Detection: A Novel Approach Based on Two-Stage Multi-View Spectral Rotation Co-clustering , 2017, IEEE Transactions on Image Processing.

[11]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Quan Pan,et al.  Classifier Fusion With Contextual Reliability Evaluation , 2018, IEEE Transactions on Cybernetics.

[13]  Yi Yang,et al.  Weakly Supervised Photo Cropping , 2014, IEEE Transactions on Multimedia.

[14]  Xuelong Li,et al.  A Review of Co-Saliency Detection Algorithms , 2018 .