Abstract
The target recognition of the human eye in real scenes is still far superior to any robot vision system. We believe that there are two major essential reasons. First, humans can observe the environment in which the object is located, get the probability of the object category. Second, human can use foveal to focus on the object and get more object detail features from the high resolution image containing the object and make it easier to identify. This paper proposes a novel method for searching and locating surrounding objects using a monocular Panning/Tilting/Zooming (PTZ) camera with free rotation and zoom functions.
Our system is an active environment-aware vision system based on Intrinsic Motivation and capable of autonomously exploring the surroundings of the camera. At the same time, by combining the visual information of foveal field of view and context field of view, the visual system observes more details and make more accurate prediction, and overcomes the limitation of low-resolution image in target recognition. Finally, our experiment proved that the visual perception system incorporating the curiosity mechanism is superior to the common perception method in terms of time overhead and learning ability.
The research was partly supported by the program for Professor of Special Appointment (Eastern Scholar, 15HJPY-MS02) at Shanghai Institutions of Higher Learning, National Natural Science Foundation of China (No. 61775139).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ekvall, S., Kragic, D., Jensfelt, P.: Object detection and mapping for service robot tasks. Robotica 25(2), 175–187 (2007)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194 (2001)
Frintrop, S., Rome, E., Christensen, H.I.: Computational visual attention systems and their cognitive foundations: a survey. ACM Trans. Appl. Percept. (TAP) 7(1), 6 (2010)
Minut, S., Mahadevan, S.: A reinforcement learning model of selective visual attention. In: Proceedings of the Fifth International Conference on Autonomous Agents. ACM (2001)
Hueber, N., et al.: Bio-inspired approach for intelligent unattended ground sensors. In: Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, vol. 9494. International Society for Optics and Photonics (2015)
Young, M.: The Technical Writer’s Handbook: Writing with Style and Clarity. University Science Books (2002)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint (2017)
Baldassarre, G., Mirolli, M. (eds.): Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2013)
Oudeyer, P.-Y., Kaplan, F., Hafner, V.V.: Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11(2), 265–286 (2007)
Skinner, B.F.: An Experimental Analysis. The Behavior of Organisms. BF Skinner Foundation (1990)
Rosen, B.E., Goodwin, J.M., Vidal, J.J.: Machine operant conditioning. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Piscataway (1988)
Tao, S., et al.: A study on autonomous learning mechanism of cognitive robot. In: 2015 27th Chinese Control and Decision Conference (CCDC). IEEE (2015)
Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Cangelosi, A., Schlesinger, M.: Developmental Robotics: From Babies to Robots. The MIT Press (2015)
Brogden, J.W.: Principles of behavior. J. Consult. Psychol. 8(5), 330–330 (1944)
Berlyne, D.E.: Curiosity and exploration. Science 153(3731), 25–33 (1966)
Goodfellow, I.J., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems. MIT Press (2014)
Bajcsy, R., Aloimonos, Y., Tsotsos, J.K.: Revisiting active perception. Auton. Rob. 42(2), 177–196 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pei, D., Jiang, L. (2019). Intrinsically Motivated Active Perception for Multi-areas View Tasks. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-35288-2_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35287-5
Online ISBN: 978-3-030-35288-2
eBook Packages: Computer ScienceComputer Science (R0)