Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature

Vision-Based Traffic Data Collection Sensor for Automotive Applications

This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry

Verificación de vehículos mediante técnicas de visión artificial

En este trabajo, se proponen sistemas de verificación de vehículos mediante métodos basados en aprendizaje. En primer lugar se realiza un estudio del estado del arte para conocer los problemas actuales en la materia. Después, se muestra la arquitectura de los sistemas que se divide en dos etapas: extracción de características y clasificación. En la primera etapa se realiza una breve exposición de los tipos de características que se van a implementar (simetría, bordes, análisis de componentes principales (PCA) e histogramas de gradientes orientados (HOG)). La etapa de clasificación consiste en una explicación teórica de los clasificadores utilizados en nuestro sistema. Posteriormente, se realiza el desarrollo de estos sistemas, efectuando mejoras para cada uno de ellos. Para el sistema basado en simetría se plantean dos métodos diferentes, introduciéndose una mejora en el segundo método, que consiste en una diferenciación entre ejes compuestos por uno y dos píxeles, junto con una penalización en los valores de simetría para conseguir una mayor diferenciación entre las clases. Respecto al sistema basado en bordes, se utilizan únicamente bordes verticales, donde se analiza el uso de vectores reducidos. Por otra parte, se presenta el uso de la matriz de correlaciones para desarrollar el sistema basado en PCA. En el sistema basado en HOG se estudia qué parámetros son los adecuados para el descriptor en el caso particular de vehículos, proponiéndose descriptores eficientes basados en esta configuración, que pueden ser implementados en sistemas en tiempo real. Finalmente, con los resultados obtenidos en el paso previo se procede a un análisis para los distintos métodos presentando sus principales características y limitaciones.In this work, a vehicle verification systems using learning methods are proposed. First, a study of related work has been done. Afterwards, the arquitecture of these systems is explained. The arquitecure is divided in two stages: feature extraction and clasification. In the first stage, a brief summary of the different features that will be implemented (simmetry, edges, principal components analysis (PCA) and histograms of oriented gradients (HOG)) is given. The second stage is a theoretical explanation of the classifiers used in this system. Subsequently, the systems are developed with new improvements. Two different methods are proposed for the system based on symmetry. An improvement is introduced for the second method that is a differentiation between compounds axes by one and two pixels, also a penalty is introduced into the values of symmetry for greater differentiation between classes. Regarding the system based on edges, vertical edges are used, where the performance reducing the size of the vectors is analyzed. Moreover, the correlation matrix is used to develop the system based on PCA. In the system based on HOG, in the particular case of vehicles, appropiate parameters for the descriptor are studied, proposing efficient descriptors based on this configuration that can be implemented in real-time systems. Finally, the results obtained in the previous step are analyzed for each of the methods, and their main characteristics and limitations are described