Abstract
Object tracking is used to locate the position of an object over a period of time using the association of an object of interest over consecutive frames. In the last years, several methods were proposed to track objects in rectangular videos. This paper presents is an object tracking method within 360-degree videos using a state-of-the-art tracking-by-detection paradigm. This method uses two trackers namely Kalman filter and Lucas-Kanade methods to handle challenges in the 360-degree videos. The proposed method uses a deep learning object detector for extraction of prior information of the object of interest. The information is then used, to track the object of interest using a combination of the two trackers of the Kalman filter and Lucas-Kanade. The experiments show that this combination improves the tracker stability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
https://360fly.com/videos. Accessed 30 Sept 2010
http://ffmpeg.org/. Accessed Aug 2018
Bradski G (2000) The OpenCV library. Dr. Dobb’s J Softw Tools 25:120–125
Bradski GR (1998) Real time face and object tracking as a component of a perceptual user interface. In: Proceedings fourth IEEE workshop on applications of computer vision, WACV 1998 (Cat. No. 98EX201), pp 214–219. https://doi.org/10.1109/ACV.1998.732882
Brendel W, Amer M, Todorovic S (2011) Multiobject tracking as maximum weight independent set. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1273–1280
Chavda HK, Dhamecha M (2017) Moving object tracking using PTZ camera in video surveillance system. In: 2017 international conference on energy, communication, data analytics and soft computing (ICECDS). IEEE, pp 263–266
Chen L, Wang W, Panin G, Knoll A (2015) Hierarchical grid-based multi-people tracking-by-detection with global optimization. IEEE Trans Image Process 24(11):4197–4212
Delforouzi A, Tabatabaei SAH, Shirahama K, Grzegorzek M (2018) A polar model for fast object tracking in 360-degree camera images. Multimed Tools Appl 2018:1–23
El Kadmiri O, Masmoudi L (2011) An omnidirectional image unwrapping approach. In: 2011 international conference on multimedia computing and systems (ICMCS). IEEE, pp 1–4
Liu KC, Shen YT, Chen LG (2018) Simple online and realtime tracking with spherical panoramic camera. In: 2018 IEEE international conference on consumer electronics (ICCE). IEEE, pp 1–6
Maybeck PS (1982) Stochastic models, estimation, and control, vol 3. Academic Press, Cambridge
Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. CoRR abs/1804.02767. http://arxiv.org/abs/1804.02767
Rojas R (2010) Lucas-kanade in a nutshell. Freie Universit at Berlinn, Department of Computer Science, Technical Report
Swalaganata G, Affriyenni Y Moving object tracking using hybrid method
Wen L, Li W, Yan J, Lei Z, Yi D, Li SZ (2014) Multiple target tracking based on undirected hierarchical relation hypergraph. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1282–1289
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Delforouzi, A., Holighaus, D., Grzegorzek, M. (2020). Deep Learning for Object Tracking in 360 Degree Videos. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-19738-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19737-7
Online ISBN: 978-3-030-19738-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)