Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework

Comparison of fusion methods for thermo-visual surveillance tracking

In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme

Dense Feature Aggregation and Pruning for RGBT Tracking

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985

The seventh visual object tracking VOT2019 challenge results

180The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOTST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on 'real-time' shortterm tracking in RGB, (iii) VOT-LT2019 focused on longterm tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website.openopenKristan M.; Matas J.; Leonardis A.; Felsberg M.; Pflugfelder R.; Kamarainen J.-K.; Zajc L.C.; Drbohlav O.; Lukezic A.; Berg A.; Eldesokey A.; Kapyla J.; Fernandez G.; Gonzalez-Garcia A.; Memarmoghadam A.; Lu A.; He A.; Varfolomieiev A.; Chan A.; Tripathi A.S.; Smeulders A.; Pedasingu B.S.; Chen B.X.; Zhang B.; Baoyuanwu B.; Li B.; He B.; Yan B.; Bai B.; Li B.; Li B.; Kim B.H.; Ma C.; Fang C.; Qian C.; Chen C.; Li C.; Zhang C.; Tsai C.-Y.; Luo C.; Micheloni C.; Zhang C.; Tao D.; Gupta D.; Song D.; Wang D.; Gavves E.; Yi E.; Khan F.S.; Zhang F.; Wang F.; Zhao F.; De Ath G.; Bhat G.; Chen G.; Wang G.; Li G.; Cevikalp H.; Du H.; Zhao H.; Saribas H.; Jung H.M.; Bai H.; Yu H.; Peng H.; Lu H.; Li H.; Li J.; Li J.; Fu J.; Chen J.; Gao J.; Zhao J.; Tang J.; Li J.; Wu J.; Liu J.; Wang J.; Qi J.; Zhang J.; Tsotsos J.K.; Lee J.H.; Van De Weijer J.; Kittler J.; Ha Lee J.; Zhuang J.; Zhang K.; Wang K.; Dai K.; Chen L.; Liu L.; Guo L.; Zhang L.; Wang L.; Wang L.; Zhang L.; Wang L.; Zhou L.; Zheng L.; Rout L.; Van Gool L.; Bertinetto L.; Danelljan M.; Dunnhofer M.; Ni M.; Kim M.Y.; Tang M.; Yang M.-H.; Paluru N.; Martinel N.; Xu P.; Zhang P.; Zheng P.; Zhang P.; Torr P.H.S.; Wang Q.Z.Q.; Guo Q.; Timofte R.; Gorthi R.K.; Everson R.; Han R.; Zhang R.; You S.; Zhao S.-C.; Zhao S.; Li S.; Li S.; Ge S.; Bai S.; Guan S.; Xing T.; Xu T.; Yang T.; Zhang T.; Vojir T.; Feng W.; Hu W.; Wang W.; Tang W.; Zeng W.; Liu W.; Chen X.; Qiu X.; Bai X.; Wu X.-J.; Yang X.; Chen X.; Li X.; Sun X.; Chen X.; Tian X.; Tang X.; Zhu X.-F.; Huang Y.; Chen Y.; Lian Y.; Gu Y.; Liu Y.; Chen Y.; Zhang Y.; Xu Y.; Wang Y.; Li Y.; Zhou Y.; Dong Y.; Xu Y.; Zhang Y.; Li Y.; Luo Z.W.Z.; Zhang Z.; Feng Z.-H.; He Z.; Song Z.; Chen Z.; Zhang Z.; Wu Z.; Xiong Z.; Huang Z.; Teng Z.; Ni Z.Kristan, M.; Matas, J.; Leonardis, A.; Felsberg, M.; Pflugfelder, R.; Kamarainen, J. -K.; Zajc, L. C.; Drbohlav, O.; Lukezic, A.; Berg, A.; Eldesokey, A.; Kapyla, J.; Fernandez, G.; Gonzalez-Garcia, A.; Memarmoghadam, A.; Lu, A.; He, A.; Varfolomieiev, A.; Chan, A.; Tripathi, A. S.; Smeulders, A.; Pedasingu, B. S.; Chen, B. X.; Zhang, B.; Baoyuanwu, B.; Li, B.; He, B.; Yan, B.; Bai, B.; Li, B.; Li, B.; Kim, B. H.; Ma, C.; Fang, C.; Qian, C.; Chen, C.; Li, C.; Zhang, C.; Tsai, C. -Y.; Luo, C.; Micheloni, C.; Zhang, C.; Tao, D.; Gupta, D.; Song, D.; Wang, D.; Gavves, E.; Yi, E.; Khan, F. S.; Zhang, F.; Wang, F.; Zhao, F.; De Ath, G.; Bhat, G.; Chen, G.; Wang, G.; Li, G.; Cevikalp, H.; Du, H.; Zhao, H.; Saribas, H.; Jung, H. M.; Bai, H.; Yu, H.; Peng, H.; Lu, H.; Li, H.; Li, J.; Li, J.; Fu, J.; Chen, J.; Gao, J.; Zhao, J.; Tang, J.; Li, J.; Wu, J.; Liu, J.; Wang, J.; Qi, J.; Zhang, J.; Tsotsos, J. K.; Lee, J. H.; Van De Weijer, J.; Kittler, J.; Ha Lee, J.; Zhuang, J.; Zhang, K.; Wang, K.; Dai, K.; Chen, L.; Liu, L.; Guo, L.; Zhang, L.; Wang, L.; Wang, L.; Zhang, L.; Wang, L.; Zhou, L.; Zheng, L.; Rout, L.; Van Gool, L.; Bertinetto, L.; Danelljan, M.; Dunnhofer, M.; Ni, M.; Kim, M. Y.; Tang, M.; Yang, M. -H.; Paluru, N.; Martinel, N.; Xu, P.; Zhang, P.; Zheng, P.; Zhang, P.; Torr, P. H. S.; Wang, Q. Z. Q.; Guo, Q.; Timofte, R.; Gorthi, R. K.; Everson, R.; Han, R.; Zhang, R.; You, S.; Zhao, S. -C.; Zhao, S.; Li, S.; Li, S.; Ge, S.; Bai, S.; Guan, S.; Xing, T.; Xu, T.; Yang, T.; Zhang, T.; Vojir, T.; Feng, W.; Hu, W.; Wang, W.; Tang, W.; Zeng, W.; Liu, W.; Chen, X.; Qiu, X.; Bai, X.; Wu, X. -J.; Yang, X.; Chen, X.; Li, X.; Sun, X.; Chen, X.; Tian, X.; Tang, X.; Zhu, X. -F.; Huang, Y.; Chen, Y.; Lian, Y.; Gu, Y.; Liu, Y.; Chen, Y.; Zhang, Y.; Xu, Y.; Wang, Y.; Li, Y.; Zhou, Y.; Dong, Y.; Xu, Y.; Zhang, Y.; Li, Y.; Luo, Z. W. Z.; Zhang, Z.; Feng, Z. -H.; He, Z.; Song, Z.; Chen, Z.; Zhang, Z.; Wu, Z.; Xiong, Z.; Huang, Z.; Teng, Z.; Ni, Z