Abstract
Expansion of capabilities of intelligent surveillance systems and research in human motion analysis requires massive amounts of video data for training of learning methods and classifiers and for testing the solutions under realistic conditions. While there are many publicly available video sequences which are meant for training and testing, the existing video datasets are not adequate for real world problems, due to low realism of scenes and acted out human behaviors, relatively small sizes of datasets, low resolution and sometimes low quality of video.
This article presents VMASS, a dataset of large volume high definition video sequences, which is continuously updated by data acquisition from multiple cameras monitoring urban areas of high activity. The VMASS dataset is described along with the acquisition and continuous updating processes and compared to other available video datasets of similar purpose. Also described is the sequence annotation process. The amount of video data collected so far exceeds 4000 hours, 540 million frames and 2 million recorded events, with 3500 events annotated manually using about 150 event types.
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References
Schuldt, C., Laptevand, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: ICPR (2004)
Laptev, I., Perez, P.: Retrieving actions in movies. In: ICCV, pp. 1–8 (2007)
Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.-C., Lee, J.T., Mukherjee, S., Aggarwal, J.K., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., Desai, M.: A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR 2011, pp. 3153–3160 (2011)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. PAMI 29(12), 22472253 (2007)
Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos “in the Wild”. In: CVPR 2009 (2009)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. CVIU 104(2), 249–257 (2006)
Ke, Y., Sukthankar, R., Hebert, M.: Volumetric Features for Video Event Detection. IJCV 88(1) (2010)
Fisher, R.B.: The PETS04 Surveillance Ground-Truth Data Sets (2004)
Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: MIR 2006 (2006)
Hartley, R.I.: Self-Calibration from Multiple Views with a Rotating Camera. In: Eklundh, J.-O. (ed.) ECCV 1994, Part I. LNCS, vol. 800, pp. 471–478. Springer, Heidelberg (1994)
Oberkampf, D., DeMenthon, D.F., Davis, L.S.: Iterative Pose Estimation Using Coplanar Feature Points. Computer Vision and Image Understanding 63(3), 495–511 (1996)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems (2001)
Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction. In: International Conference Pattern Recognition, UK (August 2004)
Davis, J.W., Bradski, G.R.: Motion Segmentation and Pose Recognition with Motion History Gradients. Machine Vision and Applications (2002)
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Kulbacki, M., Segen, J., Wereszczyński, K., Gudyś, A. (2014). VMASS: Massive Dataset of Multi-camera Video for Learning, Classification and Recognition of Human Actions. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_58
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DOI: https://doi.org/10.1007/978-3-319-05458-2_58
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