Multi-view Vehicle Detection based on Part Model with Active Learning

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods

Vehicle pose estimation for vehicle detection and tracking based on road direction

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC)

Vehicle pose estimation for vehicle detection and tracking based on road direction

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC)

Sistema de análisis de video para medición automática de intensidad de tráfico

El tráfico vehicular se está haciendo día a día más complicado para nuestra sociedad, ya que la cantidad de vehículos aumenta sin cesar y se producen congestionamientos que aumentan los tiempos de traslado, producen retrasos indeseados que irritan ánimos, dificultan los movimientos de servicios esenciales como ambulancias, bomberos, policía, etc. En la ciudad de Santa Fe se ha identificado la necesidad de una reestructuración de las políticas aplicadas a la circulación y se está trabajando en un Programa de Movilidad Urbana. Una de las medidas iniciales es cuantificar objetivamente la densidad de tráfico en arterias principales. En este trabajo se presenta el diseño, implementación y resultados iniciales de un sistema de análisis automático de videos obtenidos de las cámaras urbanas. Mediante procesamiento digital de imágenes se identifica los vehículos y se realiza el conteo discriminando en coches de gran porte (camiones y colectivos), mediano porte (automóviles y camionetas) y pequeño porte (motocicletas y bicicletas). El proyecto se encuentra en fase de prototipo funcional, donde se llegó a implementar el sistema base de procesamiento mencionado más una interfase gráfica de usuario que permite la visualización de videos, la configuración de parámetros de funcionamiento y la obtención automática de la intensidad de tráfico en un intervalo de tiempo definido.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Sistema de análisis de video para medición automática de intensidad de tráfico

El tráfico vehicular se está haciendo día a día más complicado para nuestra sociedad, ya que la cantidad de vehículos aumenta sin cesar y se producen congestionamientos que aumentan los tiempos de traslado, producen retrasos indeseados que irritan ánimos, dificultan los movimientos de servicios esenciales como ambulancias, bomberos, policía, etc. En la ciudad de Santa Fe se ha identificado la necesidad de una reestructuración de las políticas aplicadas a la circulación y se está trabajando en un Programa de Movilidad Urbana. Una de las medidas iniciales es cuantificar objetivamente la densidad de tráfico en arterias principales. En este trabajo se presenta el diseño, implementación y resultados iniciales de un sistema de análisis automático de videos obtenidos de las cámaras urbanas. Mediante procesamiento digital de imágenes se identifica los vehículos y se realiza el conteo discriminando en coches de gran porte (camiones y colectivos), mediano porte (automóviles y camionetas) y pequeño porte (motocicletas y bicicletas). El proyecto se encuentra en fase de prototipo funcional, donde se llegó a implementar el sistema base de procesamiento mencionado más una interfase gráfica de usuario que permite la visualización de videos, la configuración de parámetros de funcionamiento y la obtención automática de la intensidad de tráfico en un intervalo de tiempo definido.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Techniques for Detection and Tracking of Multiple Objects

During the past decade, object detection and object tracking in videos have received a great deal of attention from the research community in view of their many applications, such as human activity recognition, human computer interaction, crowd scene analysis, video surveillance, sports video analysis, autonomous vehicle navigation, driver assistance systems, and traffic management. Object detection and object tracking face a number of challenges such as variation in scale, appearance, view of the objects, as well as occlusion, and changes in illumination and environmental conditions. Object tracking has some other challenges such as similar appearance among multiple targets and long-term occlusion, which may cause failure in tracking. Detection-based tracking techniques use an object detector for guiding the tracking process. However, existing object detectors usually suffer from detection errors, which may mislead the trackers, if used for tracking. Thus, improving the performance of the existing detection schemes will consequently enhance the performance of detection-based trackers. The objective of this research is two fold: (a) to investigate the use of 2D discrete Fourier and cosine transforms for vehicle detection, and (b) to develop a detection-based online multi-object tracking technique. The first part of the thesis deals with the use of 2D discrete Fourier and cosine transforms for vehicle detection. For this purpose, we introduce the transform-domain two-dimensional histogram of oriented gradients (TD2DHOG) features, as a truncated version of 2DHOG in the 2DDFT or 2DDCT domain. It is shown that these TD2DHOG features obtained from an image at the original resolution and a downsampled version from the same image are approximately the same within a multiplicative factor. This property is then utilized in developing a scheme for the detection of vehicles of various resolutions using a single classifier rather than multiple resolution-specific classifiers. Extensive experiments are conducted, which show that the use of the single classifier in the proposed detection scheme reduces drastically the training and storage cost over the use of a classifier pyramid, yet providing a detection accuracy similar to that obtained using TD2DHOG features with a classifier pyramid. Furthermore, the proposed method provides a detection accuracy that is similar or even better than that provided by the state-of-the-art techniques. In the second part of the thesis, a robust collaborative model, which enhances the interaction between a pre-trained object detector and a number of particle filter-based single-object online trackers, is proposed. The proposed scheme is based on associating a detection with a tracker for each frame. For each tracker, a motion model that incorporates the associated detections with the object dynamics, and a likelihood function that provides different weights for the propagated particles and the newly created ones from the associated detections are introduced, with a view to reduce the effect of detection errors on the tracking process. Finally, a new image sample selection scheme is introduced in order to update the appearance model of a given tracker. Experimental results show the effectiveness of the proposed scheme in enhancing the multi-object tracking performance