Abstract
As the number of mobile cameras increases the task of camera independent motion detection is becoming more important. However, there are a lot of problems causing most algorithms unreliable. Our paper investigates the effect of the aperture problem and proposes a projection-based technique to increase the efficiency. We show, with the help of artificial test data, that partial motion information can also be used for motion detection resulting in better performance.
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References
Bouguet, J.-Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm. Intel Corporation Microprocessor Research Labs
Fejes, S., Davis, L.S.: Detection of independent motion using directional motion estimation. Computer Vision and Image Understanding 78, 101–120 (1999)
Foresti, G.L., Micheloni, C.: A robust feature tracker for active surveillance of outdoor scenes. Electronic Letters on Computer Vision and Image Analysis, 21–34 (2003)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Vision Conf., pp. 147–151 (1988)
Irani, M., Anandan, P.: A unified approach to moving object detection in 2d and 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 577–588 (1998)
Jung, B., Sukhatme, G.S.: Detecting moving objects using a single camera on a mobile robot an outdoor environment. In: Proceedings of the International Conference on Intelligent Autonomous Systems, pp. 980–987 (2004)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Moravec, H.: Obstacle avoidance and navigation in the real world by a seeing robot rover. PhD thesis, Stanford University (1980)
Shi, J., Tomasi, C.: Good features to track. In: Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 747–757 (2000)
Szolgay, D., Benois-Pineau, J., Megret, R., Gaestel, Y.: Detection of moving foreground objects in videos with strong camera motion. Pattern Analysis and Applications (2011)
Tomasi, C., Kanade, T.: Detection and Tracking of Point Features. Pattern Recognition 37, 165–168 (2004)
Trajkoviæ, M., Hedley, M.: Fast corner detection. Image and Vision Computing, 75–87 (1998)
Zhou, H., Wallace, A.M., Green, P.R.: Efficient tracking and ego-motion recovery using gait analysis. Signal Processing 89(12), 2367–2384 (2009)
Zivkovic, Z., van der Heijden, F.: Better features to track by estimating the tracking convergence region. In: 16th International Conference on Pattern Recognition, pp. 635–638 (2002)
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Czúni, L., Gál, M. (2012). Independent Motion Detection in the Light of the Aperture Problem. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_24
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DOI: https://doi.org/10.1007/978-3-642-31295-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31294-6
Online ISBN: 978-3-642-31295-3
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