Fully-convolutional siamese networks for object tracking

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9914 LNCS, Issue , 2016, Pages 850-865

  Bertinetto, Luca ^a   Valmadre, Jack ^a   Henriques, João F ^a   Vedaldi, Andrea ^a   Torr, Philip H S ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Deep learning; Object tracking; Siamese network; Similarity learning

Indexed keywords

BENCHMARKING; COMPUTER VISION; CONVOLUTION; DEEP LEARNING; STOCHASTIC SYSTEMS; TRACKING (POSITION);

ARBITRARY OBJECTS; CONVOLUTIONAL NETWORKS; DEEP-LEARNING; EXPRESSIVE POWER; LEARN+; OBJECT TRACKING; SIAMESE-NETWORK; SIMILARITY LEARNING; STOCHASTIC GRADIENT DESCENT; TRAINING DATA;

OBJECT DETECTION;

EID: 84996921146     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-48881-3_56     Document Type: Conference Paper

References (47)

1. Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. PAMI 36 (7), 1442–1468 (2014)
2. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. PAMI 34 (7), 1409–1422 (2012)
3. Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV 2011. IEEE (2011)
4. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. PAMI 37 (3), 583–596 (2015)
5. Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: ICCV (2015)
6. Danelljan, M., Hager, G., Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: ICCV 2015 Workshop, pp. 58–66 (2015)
7. Wang, N., Li, S., Gupta, A., Yeung, D.Y.: Transferring rich feature hierarchies for robust visual tracking. arXiv CoRR (2015)
8. Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: ICCV (2015)
9. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. arXiv CoRR (2015)
10. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. IJCV 115 (3), 211–252 (2015)
11. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR 2013 (2013)
12. Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., Vojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: The visual object tracking VOT2015 challenge results. In: ICCV 2015 Workshop, pp. 1–23 (2015)
13. Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: An astounding baseline for recognition. In: CVPR 2014 Workshop (2014)
14. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC 2015 (2015)
15. Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: FlowNet: Learning optical flow with convolutional networks. In: ICCV 2015 (2015)
16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS 2012 (2012)
17. Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: Signature verification using a “Siamese” time delay neural network. Int. J. Pattern Recogn. Artif. Intell. (1993)
18. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to humanlevel performance in face verification. CVPR 2014, 1701–1708 (2014)
19. Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: CVPR 2015 (2015)
20. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR 2015, pp. 815–823 (2015)
21. Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. ICCV 2015, 118–126 (2015)
22. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML 2015 Deep Learning Workshop (2015)
23. Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: CVPR 2016, pp. 5695–5703 (2016)
24. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML 2015, pp. 448–456 (2015)
25. Gan, Q., Guo, Q., Zhang, Z., Cho, K.: First step toward model-free, anonymous object tracking with recurrent neural networks. arXiv CoRR (2015)
26. Kahou, S.E., Michalski, V., Memisevic, R.: RATM: Recurrent Attentive Tracking Model. arXiv CoRR (2015)
27. Denil, M., Bazzani, L., Larochelle, H., de Freitas, N.: Learning where to attend with deep architectures for image tracking. Neural Computation (2012)
28. Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. arXiv CoRR (2016)
29. Chen, K., Tao, W.: Once for all: a two-flow convolutional neural network for visual tracking. arXiv CoRR (2016)
30. Tao, R., Gavves, E., Smeulders, A.W.M.: Siamese instance search for tracking. arXiv CoRR (2016)
31. Vedaldi, A., Lenc, K.: MatConvNet-Convolutional Neural Networks for MATLAB (2015)
32. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing humanlevel performance on ImageNet classification. In: ICCV 2015 (2015)
33. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: Staple: Complementary learners for real-time tracking. In: CVPR 2016 (2016)
34. Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: CVPR 2015 (2015)
35. Zhu, G., Wang, J., Wu, Y., Lu, H.: Collaborative correlation tracking. In: BMVC 2015 (2015)
36. Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: Visual tracking using attention-modulated disintegration and integration. In: CVPR 2016 (2016)
37. Ning, J., Yang, J., Jiang, S., Zhang, L., Yang, M.H.: Object tracking via dual linear structured SVM and explicit feature map. In: CVPR 2016 (2016)
38. Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC 2014 (2014)
39. Huang, D., Luo, L., Wen, M., Chen, Z., Zhang, C.: Enable scale and aspect ratio adaptability in visual tracking with detection proposals
40. Liris, F.: The visual object tracking vot2014 challenge results (2014)
41. Zhu, G., Porikli, F., Li, H.: Tracking randomly moving objects on edge box proposals. arXiv CoRR (2015)
42. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV 2015, pp. 4310–4318 (2015)
43. Hua, Y., Alahari, K., Schmid, C.: Online object tracking with proposal selection. ICCV 2015, 3092–3100 (2015)
44. Wang, N., Yeung, D.Y.: Ensemble-based tracking: aggregating crowdsourced structured time series data. ICML 2014, 1107–1115 (2014)
45. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: ECCV 2014 Workshops (2014)
46. Li, A., Lin, M., Wu, Y., Yang, M.H., Yan, S.: NUS-PRO: a new visual tracking challenge. PAMI 38 (2), 335–349 (2016)
47. Possegger, H., Mauthner, T., Bischof, H.: In defense of color-based model-free tracking. In: CVPR 2015 (2015)