Abstract
Video surveillance has significant application prospects such as security, law enforcement, and traffic monitoring. Visual traffic surveillance using computer vision techniques can be non-invasive, cost effective, and automated. Detecting and recognizing the objects in a video is an important part of many video surveillance systems which can help in tracking of the detected objects and gathering important information. In case of traffic video surveillance, vehicle detection and classification is important as it can help in traffic control and gathering of traffic statistics that can be used in intelligent transportation systems. Vehicle classification poses a difficult problem as vehicles have high intra-class variation and relatively low inter-class variation. In this work, we investigate five different object recognition techniques: PCA + DFVS, PCA + DIVS, PCA + SVM, LDA, and constellation-based modeling applied to the problem of vehicle classification. We also compare them with the state-of-the-art techniques in vehicle classification. In case of the PCA-based approaches, we extend face detection using a PCA approach for the problem of vehicle classification to carry out multi-class classification. We also implement constellation model-based approach that uses the dense representation of scale-invariant feature transform (SIFT) features as presented in the work of Ma and Grimson (Edge-based rich representation for vehicle classification. Paper presented at the international conference on computer vision, 2006, pp. 1185–1192) with slight modification. We consider three classes: sedans, vans, and taxis, and record classification accuracy as high as 99.25% in case of cars vs vans and 97.57% in case of sedans vs taxis. We also present a fusion approach that uses both PCA + DFVS and PCA + DIVS and achieves a classification accuracy of 96.42% in case of sedans vs vans vs taxis. 68T10; 68T45; 68U10
Highlights
Visual traffic surveillance has attracted significant interest in computer vision, because of its significant application prospects
The two main contributions of our work are the following: (1) We present several approaches (PCA + distance from vehicle space (DFVS), principal component analysis (PCA) + distance in vehicle space (DIVS), PCA + support vector machine (SVM), linear discriminant analysis (LDA), and constellation model) and improvements over the published results that used state-of-the-art techniques
We extend the face detection based on PCA and implement three different techniques: PCA + DFVS, PCA + DIVS, and PCA + SVM
Summary
Visual traffic surveillance has attracted significant interest in computer vision, because of its significant application prospects. They used a PCA-based vehicle classification framework They implemented two classification algorithms: eigenvehicle and PCA-SVM to classify vehicle objects into trucks, passenger cars, vans, and pickups. These two methods exploit the distinguishing power of principal component analysis (PCA) at different granularities with different learning mechanisms. They used SIFT features to train the constellation models that were used to classify the vehicles They considered two cases: cars vs vans and sedans vs taxis. The feature vectors were used to train a SVM classifier which was able to produce results better than those presented in [4] in cars vs vans case This approach is global feature based; it is not best suited for cases with partial occlusion. We define each eigenspace as eigenvehicle [18]
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