Integrated convolutional neural network model with statistical moments layer for vehicle classification

Jan 31, 2020

Publisher SPIE

Vehicle classification is an important topic which is still under research consideration because of its role in road surveillance, security system, traffic monitoring, and accident prevention. In this paper, we propose a deep learning model for vehicles classification using the Convolutional Neural Networks (CNN) integrated with a statistical moments layer. We referred to the model as ICNN. As an additional layer, the moments layer extracts statistical moments features from the feature maps obtained from convolutions layers. The moments layer is fed the fully-connected classifier of the network which is fine-tuned to get better results. Our Integrated CNN model (ICNN) achieves 97.1% accuracy compared to the most popular algorithms used in this field such as K Nearest Neighbour (KNN), and Support Vector Machine (SVM), which known as good tools for object classification.

Camera Model Identification With The Use of Deep Convolutional Neural Networks

Dec 4, 2016

Publisher IEEE

—In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike traditional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. A layer of preprocessing is added to the CNN model, and consists of a high pass filter which is applied to the input image. Before feeding the CNN, we examined the CNN model with two types of residuals. The convolution and classification are then processed inside the network. The CNN outputs an identification score for each camera model. Experimental comparison with a classical two steps machine learning approach shows that the proposed method can achieve significant detection performance. The well known object recognition CNN models, AlexNet and GoogleNet, are also examined. Index Terms—Camera Identification, Deep Learning, Convolutional Neural Network, Fully Connected Network.

CAMERA MODEL IDENTIFICATION BASED MACHINE LEARNING APPROACH WITH HIGH ORDER STATISTICS FEATURES

Aug 29, 2016

Publisher IEEE

Source camera identification methods aim at identifying the camera used to capture an image. In this paper we developed a method for digital camera model identification by extracting three sets of features in a machine learning scheme. These features are the co-occurrences matrix, some features related to CFA interpolation arrangement, and conditional probability statistics. These features give high order statistics which supplement and enhance the identification rate. The method is implemented with 14 camera models from Dresden database with multi class SVM classifier. A comparison is performed between our method and a camera fingerprint correlationbased method which only depends on PRNU extraction. The experiments prove the strength of our proposition since it achieves higher accuracy than the correlation-based method