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Proceedings Paper

A multi-attribute based methodology for vehicle detection and identification
Author(s): Vinayak Elangovan; Bashir Alsaidi; Amir Shirkhodaie
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Paper Abstract

Robust vehicle detection and identification is required for the intelligent persistent surveillance systems. In this paper, we present a Multi-attribute Vehicle Detection and Identification technique (MVDI) for detection and classification of stationary vehicles. The proposed model uses a supervised Hamming Neural Network (HNN) for taxonomy of shape of the vehicle. Vehicles silhouette features are employed for the training of the HNN from a large array of training vehicle samples in different type, scale, and color variation. Invariant vehicle silhouette attributes are used as features for training of the HNN which is based on an internal Hamming Distance and shape features to determine degree of similarity of a test vehicle against those it’s selectively trained with. Upon detection of class of the vehicle, the other vehicle attributes such as: color and orientation are determined. For vehicle color detection, provincial regions of the vehicle body are used for matching color of the vehicle. For the vehicle orientation detection, the key structural features of the vehicle are extracted and subjected to classification based on color tune, geometrical shape, and tire region detection. The experimental results show the technique is promising and has robustness for detection and identification of vehicle based on their multi-attribute features. Furthermore this paper demonstrates the importance of the vehicle attributes detection towards the identification of Human-Vehicle Interaction events.

Paper Details

Date Published: 23 May 2013
PDF: 10 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451E (23 May 2013); doi: 10.1117/12.2018091
Show Author Affiliations
Vinayak Elangovan, Tennessee State Univ. (United States)
Bashir Alsaidi, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)

Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)

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