Prashant Borkar

Work place: Department of CSE, GHRCE, Nagpur, India

E-mail: Prashant.borkar@raisoni.net

Website:

Research Interests: Graphics Processing Unit, Computer Graphics and Visualization, Computer Vision

Biography

Prashant Borkar, male, is a PG student at GHRCE, Nagpur. His research interests include Intelligent Transportation Systems, Signal Processing and Computer Graphics He has published more than 10 technical research papers in International Conferences and Journals.

Author Articles
Acoustic Signal based Traffic Density State Estimation using SVM

By Prashant Borkar L. G. Malik

DOI: https://doi.org/10.5815/ijigsp.2013.08.05, Pub. Date: 28 Jun. 2013

Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). Support Vector Machines (SVM) is used as classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.

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