Abstract
Vehicle re-identification (Re-Id) is the key module in an intelligent transportation system (ITS). Due to its versatile applicability in metropolitan cities, this task has received increasing attention these days. It aims to identify whether the specific vehicle has already appeared over the surveillance network or not. Mostly, the vehicle Re-Id method are evaluated on a single dataset, in which training and testing of the model is performed on the same dataset. However in practice, this negatively effects model generalization ability due to biased datasets along with the significant difference between training and testing data; hence, the model becomes weak in a practical environment. To demonstrate this issue, we have empirically shown that the current vehicle Re-Id datasets are usually strongly biased. In this regard, we also conduct an extensive study on the cross and the same dataset to examine the impact on the performance of the vehicle Re-Id system, considering existing methods. To address the problem, in this paper, we have proposed an approach with augmentation of the training dataset to reduce the influence of pose, angle, camera color response, and background information in vehicle images; whereas, spatio-temporal patterns of unlabelled target datasets are learned by transferring siamese neural network classifiers trained on a source-labelled dataset. We finally calculate the composite similarity score of spatio-temporal patterns with siamese neural-network-based classifier visual features. Extensive experiments on multiple datasets are examined and results suggest that the proposed approach has the ability to generalize adequately.
Highlights
In metropolitan cities, cameras are widely deployed in numerous areas for activity monitoring, from home surveillance systems to small and large business applications
We attempt to investigate the vehicle Re-Id dataset bias problem using deep convolutional neural network (CNN) models to show the significance of cross-dataset vehicle Re-Id study; We conduct deep empirical study on cross- and single-dataset vehicle Re-Id to further examine the impact on the performance of existing methods; We propose the data augmentation method to reduce the influence of pose, angle, camera color response, and background on a model in vehicle images; We present a novel model that combines captured spatio-temporal patterns and siamese neural network classifiers features to achieve significant improvement in cross-dataset vehicle Re-Id
We developed the whole framework in Python which is widely used in deep learning and, mostly, the libraries used are scikit-learn, SciPy, matplotlib, and NumPy
Summary
Cameras are widely deployed in numerous areas for activity monitoring, from home surveillance systems to small and large business applications. A significant number of cameras are used for security purposes in public places, like parking lots, downtown, airports, and other sensitive areas. Camera surveillance is one of the core modules in public transportation systems and has a large capability to contribute to the planning and controlling the traffic networks. The main goal of the surveillance system application is to develop such type of intelligent system that automates the human. 22 of of 20 main goal of the surveillance system application is to develop such type of intelligent system that decision-taking mechanism. The should according automates the human decision‐taking mechanism
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