A Multi-attribute based methodology for vehicle detection & identification

Vinayak Elangovan, Bashir Alsaidi, Amir Shirkhodaie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition XXII
StatePublished - 2013
EventSignal Processing, Sensor Fusion, and Target Recognition XXII - Baltimore, MD, United States
Duration: Apr 29 2013May 2 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherSignal Processing, Sensor Fusion, and Target Recognition XXII
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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