Safe Driving using Vision-based Hand Gesture Recognition System in Non-uniform Illumination Conditions

Shalini Anant; Shanmugham Veni

doi:10.5614/itbj.ict.res.appl.2018.12.2.4

Authors

Shalini Anant Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112
Shanmugham Veni Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2018.12.2.4

Keywords:

advanced driver assistance system, Android application, hand gesture recognition, homomorphic filtering, non-uniform illumination, Raspberry Pi 3, vision-based approach.

Abstract

Nowadays, there is tremendous growth in in-car interfaces for driver safety and comfort, but controlling these devices while driving requires the driver's attention. One of the solutions to reduce the number of glances at these interfaces is to design an advanced driver assistance system (ADAS). A vision-based touch-less hand gesture recognition system is proposed here for in-car human-machine interfaces (HMI). The performance of such systems is unreliable under ambient illumination conditions, which change during the course of the day. Thus, the main focus of this work was to design a system that is robust towards changing lighting conditions. For this purpose, a homomorphic filter with adaptive thresholding binarization is used. Also, gray-level edge-based segmentation ensures that it is generalized for users of different skin tones and background colors. This work was validated on selected gestures from the Cambridge Hand Gesture Database captured in five sets of non-uniform illumination conditions that closely resemble in-car illumination conditions, yielding an overall system accuracy of 91%, an average frame-by-frame accuracy of 81.38%, and a latency of 3.78 milliseconds. A prototype of the proposed system was implemented on a Raspberry Pi 3 interface together with an Android application, which demonstrated its suitability for non-critical in-car interfaces like infotainment systems.

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Author Biographies

Shalini Anant, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112

Electronics and Communication Engineering, 2nd year Mtech Student.

Shanmugham Veni, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112

Electronics and Communication Engineering, Associate Professor

References

NHTSA, Traffic Safety Facts, A Research Note-Distracted driving 2013- National Highway Traffic Safety Administration, Tech. Rep., 2013.

TNS India Pvt. Limited for SaveLIFE foundation, Distracted Driving in India-A Study On Mobile Phone Usage, Pattern & Behaviour, 2017.

Loehmann, S., Knobel, M., Lamara, M. & Butz, A., Culturally Independent Gestures for In-Car Interactions, IFIP Conference on Human-Computer Interaction, Springer, pp. 538-545, 2013.

Ohn-Bar, E. & Trivedi, M., Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-based Approach and Evaluations, IEEE Transaction on Intelligent Transportation Systems, 15(6), pp. 1-10, 2014.

Parada-Loira, F., Gonzalez-Agulla, E. & Alba-Castro, J.L., Hand Gestures to Control Infotainment Equipment in Cars, IEEE Intelligent Vehicles Symposium (IV), Detroit, MI, United States, 2014.

Ding, Z., Chen, Y., Chen, Y-L. & Wu, X., Similar Hand Gesture Recognition by Automatically Extracting Distinctive Features, International Journal of Control, Automation and Systems, Springer, pp. 1-9 Apr. 2017.

Kaur, H. & Rani, Er. J., SLIC based Hand Gesture Recognition with Artificial Neural Network, International Journal of Science Technology & Engineering, 3(3), pp. 103-107, 2016.

John, V., Boyali, A., Mita, S., Imanishi, M. & Sanma, N., Deep Learning-based Fast Hand Gesture Recognition using Representative Frames, IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA), Queensland, Australia, Dec. 2016.

Arnawa, I.B.K.S., Image Enhancement Using Homomorphic Filtering and Adaptive Median Filtering for Balinese Papyrus (Lontar), International Journal of Advanced Computer Science and Applications (IJACSA), 6(8), pp. 250-255, 2015.

Firdousi, R. & Parveen, S., Local Thresholding Techniques in Image Binarization, International Journal of Engineering and Computer Science, 3(3), pp. 4062-4065, 2014.

Hartanto, R. & Kartikasari, A., Android Based Real-Time Static Indonesian Sign Language Recognition System Prototype, IEEE International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia, 2016.

Tofighi, G., Afarin, N.A., Raahemifar, K. & Venetsanopoulos, A.N., Hand Pointing Detection Using Live Histogram Template of Forehead Skin, IEEE International Conference on Digital Signal Processing, Hong Kong, China, 2014.

Geraldine Shirley N. & Jayanthy, S., Virtual Control Hand Gesture Recognition System Using Raspberry Pi, ARPN Journal of Engineering and Applied Sciences, 10(7), pp. 2989-2993, 2015.

Shangeetha, R.K., Valliammai, V. & Padmavathi, S., Computer Vision Based Approach For Indian Sign Language Character Recognition, IEEE International Conference on Machine Vision and Image Processing (MVIP), pp. 181-184, 2012.

Singh, A.K., John, B.P., Venkata Subramanian, S.R., Sathish Kumar, A. & Nair, B.B., A Low-cost Wearable Indian Sign Language Interpretation System, International Conference on Robotics and Automation for Humanitarian Applications (RAHA), Article No. 7931873, 2016.

Gonzalez, R.C. & Woods, R.E., Digital Image Processing, ed. 3, Pearson Education Inc., 2008.

Hariyono, J. & Jo, K-H., Pedestrian Action Recognition using Motion Type Classification, IEEE 2nd International Conference on Cybernetics (CYBCONF), pp. 129-132, 2015.