Abstract
The robot is an intelligent machine or intelligent agent. When the concept of artificial intelligence is applied to machines, it mimics the functions performed by human such as decision making, learning, recognition, problem solving. The robot exactly mimics the function of humans. Automatic color object detection and tracking of it by listening audio instruction is a function performed by every human. The proposed system receives acoustic instruction as an input and analyzes the video frames and output, the location of a moving object within the video frames. The aim is to implement this system for robots, so it will behave like the human.
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
Kishor Jeve, PravinYannawar, Ashok Gaikwad (2017) A study on automatic color object learning and detection through acoustic instructions. International journal of engineering research and technology (IJERT): 1173–1176.
Changjiang Yang, Ramani Duraiswami and Larry Davis (2005) Efficient mean-shift tracking via a new similarity measure. IEEE computer society conference on computer vision and pattern recognition: 176–183.
R. VenkateshBabu, Anamitra Makur (2007) Kernel-Based spatial-color modeling for fast moving object tracking. IEEE international conference on acoustics, speech and signal processing.
D. Comaniciu and P Meer: Mean shift (2002) A robust approach toward feature space analysis. IEEE transactions on pattern analysis and machine intelligence: 603–619.
JeveKishor, Ashok T Gaikwad, Pravin L Yannawar (2017) Automatic color object detection and learning using continuously adaptive mean shift with color, scale and direction. International journal of computer application: 1–3.
Sahoo, J. K. Deepakrish (2014) A Speaker recognition using support vector machines. International journal of electrical, electronics and data communication. (2014).
P Borde, A Varpe, R Manza, P Yannawar (2015) Recognition of isolated words using zernikeand MFCC features for audio visual speech recognition. International journal of speech technology: 167–175.
Cheng, Yi zong (1995) Mean Shift, Mode Seeking, and Clustering. IEEE transactions on pattern analysis and machine intelligence: 790–799.
A. Elgammal, R. Duraiswami, and L. Davis (2003) Probabilistic tracking in joint feature-spatial spaces. IEEE conference on computer vision and pattern recognition: 781–788.
Chang jiang Yang, Ramani Duraiswami and Larry Davis (2005) Efficient Mean-Shift Tracking via a new similarity measure. IEEE computer society conference on computer vision and pattern recognition: 176–183.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jeve, K.S., Gaikwad, A.T., Yannawar, P.L., Kumbhar, A.B. (2019). A Robust Voice-Based Color Object Detection System for Robot. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 32. Springer, Singapore. https://doi.org/10.1007/978-981-10-8201-6_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-8201-6_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8200-9
Online ISBN: 978-981-10-8201-6
eBook Packages: EngineeringEngineering (R0)