|Title:||Real Time Feature Based Vehicle Detection and Classification from On-Road Videos|
|Keywords:||Detection and classification of vehicles, Virtual detection line (VDL), Bag of visual words (BOVW), Speeded up robust feature (SURF), Error correcting output code (ECOC), Support vector machine (SVM)|
Vision Based vehicle detection and classification has become an active
area of research for intelligent transportation system. But this task is
very difficult and challenging due to the dynamic condition of roads.
In the proposed method, a feature based cost effective detection and
classification method is proposed that is suitable for real time
applications, provide satisfactory accuracy and computationally cheap.
The proposed method uses haar-like image features and AdaBoost
classifier for detection. To reduce false positive rate, we propose to
use two virtual detection lines (VDL). In order to predict the class of a
vehicle, we propose a two level classifier where first classifier
separates bigger (bus, truck) vehicles from the smaller one (car, CNG,
rickshaw) based on some shape information of vehicles. For the second
classifier, we propose to use bag of features (BOF) model which uses the
feature efficiently and generates bag of visual words (BOVW). Shape
based features are used for first classifier and texture based feature
(SURF) is used for second classifier. Error correcting output code
(ECOC) framework is used to achieve multi class prediction with SVM to
predict the class. Extensive experiments have been carried out on
different local traffic data of varying environments to evaluate the
detection and classification performance of the proposed method.
Experimental results demonstrate that the proposed two level classifier
achieves a significant improvement in classification of heterogeneous
vehicles in terms of accuracy with a considerable execution time as
compared to other methods.