A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

BP neural network classification on passenger vehicle type based on GA of feature selection

This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA)of feature selection.We have obtained an optimal feature subset,including length,ratio of width and length,and ratio of height and length.And then we apply this optimal feature subset as well as another feature set,containing length,width and height,to the network input.Back-propagation(BP)neural network and support vector machine(SVM)are applied to classify the passenger vehicle type.There are four passenger vehicle types.This paper selects 400 samples of passenger vehicles,among which 320 samples are used as training set(each class has 80 samples)and the other 80 samples as testing set,taking the feature of the samples as network input and taking four passenger vehicle types as output.For the test,we have applied BP neural network to choose the optimal feature subset as network input,and the results show that the total classification accuracy rate can reach 96%,and the classification accuracy rate of first type can reach 100%.In this condition,we obtain a conclusion that this algorithm is better than the traditional ones[9].

A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° （12-angle in 360°） classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

Modelling behavioural interactions of drivers' in mixed traffic conditions

Mixed traffic conditions are often prevalent in developing economies such as India, China, Bangladesh, etc. and are characterised by the presence of multiple vehicle types. The presence of multiple vehicle types with varying dynamic and static characteristics results in vehicle-type dependent driving behaviours. For instance, drivers of small sized vehicles such as motorbikes accelerate and decelerate at will, maintain shorter safe gaps with the lead vehicles, and accept smaller lateral clearances to make lateral movements within and across lanes breaking the lane discipline. On the other hand, drivers of heavy vehicles such as trucks have less flexibility in performing the acceleration/deceleration and lateral movement operations. Thus, the representation of mixed traffic systems requires model- ling vehicle-type dependent driving behaviours. This paper first establishes the effect of vehicle type on the longitudinal and lateral movement behaviours of drivers using the trajectory data collected in India and subsequently presents the proposed vehicle-type dependent driver behavioural models based on the same dataset. The efficiency of the proposed models is tested by implementing them in a simulation framework compatible with non-lane-based movements of vehicles and cross validating with the field data. The results indicate better predictability of the driver behaviour and thus more realistic rep- resentation in the mixed traffic systems. Moreover, the simulator hinged upon the pro- posed behavioural models will be useful in evaluating alternate traffic improvement initiatives and help the transport planners to design the transport systems of developing countries in an efficient and sustainable manner.