Abstract
This paper presents a prototype called HOLOTECH that implements a model prediction using a limited description of the environment to support blind people. The goal is to perform fast detection and identification of obstacles to provide information about collision riskiness and location. The prediction is not probabilistic but statistic, in order to improve the inferences results. The model works fine using low-precision images drifted from real-time camera of a regular Android cell phone supported with ultrasonic sensors. The main focus of this work is how to pre-process images on the fly in order to be able to train and to tune the plastic learning module, improving the object’s trajectory prediction.
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References
Zhou Y et al (2017) An efficient data processing framework for mining the massive trajectory of moving objects. Comput Environ Urban Syst 61:129–140
Zhang J et al (2017) Probabilistic modelling of the drifting trajectory of an object under the effect of wind and current for maritime search and rescue. Ocean Eng 129:253–264
Valsamis A et al. (2016) Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction. J Sys Softw http://dx.doi.org/10.1016/j.jss.2016.06.016 (In press)
Sheng-Zheng W, Hao-Bing N, Chao-Jian S (2014) A drifting trajectory prediction model based on object shape and stochastic motion features. J Hydrodyn 26:951–959
Magdy N et al (2016) A generic trajectory similarity operator in moving object databases. Egyptian Informatics J. doi:10.1016/j.eij.2016.07.001 In press
Lettich F (2016) Detecting avoidance behaviors between moving object trajectories. Data Knowl Eng 102:22–41
Kong X et al (2016) Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener Comput Sys 61:97–107
Gan W, Lee M, Wu C, Kuo CJ (2016) Object tracking with temporal prediction and spatial lrefinement (TPSR). Sig Process Image Commun 47:303–312
Bourdonnaye FL, Setchi R, Zanni-Merk C (2016) Gaze trajectory prediction in the context of social robotics. IFAC-PapersOnLine 49:126–131
Abraham S, Lal PS (2012) Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations. Transp Res Part C 23:109–123
Mironov K, Vladimirova I, Pongratz M (2015) Processing and Forecasting the Trajectory of a Thrown Object Measured by the Stereo Vision System 48:28–35
Fu et al X (2014) Probabilistic trajectory prediction in intelligent driving. In: Proceedings of the 19th world congress of the international federation of automatic control, pp 2664-2672
Li X et al (2017) Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mech Syst Signal Process 87:118–137
Xu F, Lam K, Dai Q (2011) Video-object segmentation and 3D-trajectory estimation for monocular video sequences. Image Vis Comput 29:190–205
Ciccio CD et al (2016) Detecting flight trajectory anomalies and predicting diversions in freight transportation. Decis Support Syst 88:1–17
ArduinoNano (2015) http://arduino.cc/en/Main/ArduinoBoardNano
Igoe T (2012) Making Things Talk, 2nd edn
Krasula L, Klima M, Rogard E, Jeanblanc E (2012) MATLAB-based applications for image processing and image quality assessment part II. Experimental results
Bala A (2012) An improved watershed image segmentation technique using MATLAB. Int J Sci Eng Res 3(6)
Park JS, López De Luise D, Pérez J (2015) HOLOTECH prototype. Sound language for environment’s understanding. Int J Learn Technol Inderscience Publishers, Geneva
http://www.mathworks.com/help/vision/ug/train-a-cascade-object-detector.html. 2015
López De Luise D (2012) Ingeniería en Inteligencia Computacional (Computational Intelligence Engineering). In: Rovarini P (eds) (UTN-FRT), 104 pp
Chen Q, Kotani K, Lee FF, Ohmi T A fast search algorithm for large video database using hog based features
Abada L, Aouat S Facial shape-from-shading using features detection method. doi:10.1504/IJAIP.2016.074774, pp 3–19
Carletta Jean (1996) Assessing agreement on classification tasks: the kappa statistic. Comput Linguist 22(2):249–254
Arditi A, YingLi T (2013) User Interface preference in the design of a camera-base navigation and wayfinding Aid. J Vis Impairement Blindness
HS MAHDI Image understanding using object identification and spatial relationship
Bairagi B, Dey B, Sarkar B, Sanyal S Selection of robotic systems in fuzzy multi criteria decision-making environment doi:10.1504/IJCSYSE.2015.067798, pp 32–42
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Park, J.S., De Luise, D.L., Hemanth, J. (2018). Object Trajectory Prediction with Scarce Environment Information. In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_3
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DOI: https://doi.org/10.1007/978-3-319-61316-1_3
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