This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS), which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD), and One Class Support Vector Machine (OC-SVM) with different kernels (Radial Basis Function (RBF) and Mahalanobis Kernel (MK)) for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN), and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with an efficient computation time, while OC-SVM achieved detection rates slightly higher, but is more computationally expensive.

Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period $\tau$ , its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value; (2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

El objetivo de esta investigación es presentar el diseño y la implementación de un robot seguidor de línea basado en visión artificial con el sistema operativo para robots (ROS) y la biblioteca de visión artificial de código abierto (OpenCV). La razón para usar ROS y OpenCV es crear una plataforma robusta que pueda ser la base de robots más complejos como los vehículos de guiado automatizado (AGVs). Una cámara es el sensor que le permite al robot obtener imágenes de la ruta, que se procesan en un algoritmo para detectar la línea y obtener el error de retroalimentación del controlador Proporcional Integral y Derivativo (PID). La ventaja de utilizar una cámara en lugar de sensores infrarrojos es que la cámara devuelve un error con mayor resolución, además de que es posible detectar colores y obstáculos. El resultado de combinar una cámara, un controlador PID, ROS y OpenCV es un robot seguidor de línea estable y robusto, fácil de integrar con otros sistemas.

Network anomaly detection and classification is an important open issue of network security. Several approaches and systems based on different mathematical tools have been studied and developed. Among them, the Anomaly-Network Intrusion Detection System (A-NIDS), this monitors network traffic and compares it against an established baseline of "normal" traffic profile. Then, it is necessary to characterize the "normal" Internet traffic. This paper presents an approach for anomaly detection and classification based on: The entropy of selected features (including Shannon, Renyi and Tsallis entropies); the construction of regions from entropy data employing the Mahalanobis distance (MD) and One Class Support Vector Machine (OC-SVM) with different kernels (RBF and Mahalanobis) for normal and abnormal traffic. Regular and non-regular regions built from "normal" traffic profiles, allow the anomaly detection; whilst the classification is performed under the assumption that regions corresponding to the attack classes have been characterized previously. Although, this approach allows the use of as many features as required, only four well known significant features were selected in our case. To evaluate our approach two different data sets were used: One set of real traffic obtained from an Academic LAN, and the other a subset of the 1998 MIT-DARPA set. The selected features sets computed in our experiments provide detection rates up to 99.98% with "normal" traffic and up to 99.05% with anomalous traffic and false alarm rate of 0.019%. Experimental results show that certain values of the q parameter of the generalized entropies and the use of OC-SVM improves the detection rate of some attack classes, due to a better fit of the region to the data. Besides, our results show that MD allows to obtain high detection rates with an efficient computation time, while OC-SVM achieved detection rates lightly more precise but more expensive computationally.

Floodings in Mexico generated economic and human losses in recent years, so it is necessary to use all possible tools that can help the government to reduce all these disasters, especially human losses. Therefore, a Graphical User Interface (GUI) was developed in Matlab for the segmentation and classification of vegetation, water and city in multispectral images obtained from the Landsat 8 satellite with the intention of detecting floods and vulnerable zones of flooding. The interface performs a feature extraction, segmentation, classification, validation and visualization of the final results obtained through basic segmentation algorithms such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), in addition to performing the segmentation with one of the artificial intelligence methodologies most used in the state of the art: support vector machine (SVM) and the proposal of SVM with the k-nearest neighbors as an improvement to the algorithm.