Abstract: Nowadays, traffic analysis are relying on data collected from various traffic sensors. Among the various traffic surveillance techniques, video surveillance systems are often used for monitoring and characterizing traffic load. In this thesis, we focused on two aspects of traffic analysis without using motion features in low frame-rate videos: Traffic density flow analysis and Vehicle detection and classification. Traffic density flow analysis}: Knowing in real time when the traffic is fluid or when it jams is a key information to help authorities re-route vehicles and reduce congestion. Accurate and timely traffic flow information is strongly needed by individual travelers, the business sectors and government agencies. In this part, we investigated the possibility of monitoring highway traffic based on videos whose frame rate is too low to accurately estimate motion features. As we are focusing on analyzing traffic images and low frame-rate videos, traffic density is defined as the percentage of road being occupied by vehicles. In our previous work, we validated that traffic status is highly correlated to its texture features and that Convolutional Neural Networks (CNN) has the superiority of extracting discriminative texture features. We proposed several CNN models to segment traffic images into three different classes (road, car and background), classify traffic images into different categories (empty, fluid, heavy, jam) and predict traffic density without using any motion features. In order to generalize the model trained on a specific dataset to analyze new traffic scenes, we also proposed a novel transfer learning framework to do model adaptation. Vehicle detection and classification: The detection of vehicles pictured by traffic cameras is often the very first step of video surveillance systems, such as vehicle counting, tracking and retrieval. In this part, we explore different deep learning methods applied to vehicle detection and classification. Firstly, realizing the importance of large dataset for traffic analysis, we built and released the largest traffic dataset (MIO-TCD) in the world for vehicle localization and classification in collaboration with colleagues from Miovision inc. (Waterloo, On). With this dataset, we organized the Traffic Surveillance Workshop and Challenge in conjunction with CVPR 2017. Secondly, we evaluated several state-of-the-art deep learning methods for the classification and localization task on the MIO-TCD dataset. In light of the results, we may conclude that state-of-the-art deep learning methods exhibit a capacity to localize and recognize vehicle from a single video frame. While with a deep analysis of the results, we also identify scenarios for which state-of-the-art methods are still failing and propose concrete ideas for future work. Lastly, as saliency detection aims to highlight the most relevant objects in an image (e.g. vehicles in traffic scenes), we proposed a multi-resolution 4*5 grid CNN model for the salient object detection. The model enables near real-time high performance saliency detection. We also extend this model to do traffic analysis, experiment results show that our model can precisely segment foreground vehicles in traffic scenes. / De nos jours, l’analyse de trafic routier est de plus en plus automatisée et s’appuie sur des données issues de senseurs en tout genre. Parmi les approches d’analyse de trafic routier figurent les méthodes à base de vidéo. Les méthodes à base de vidéo ont pour but d’identifier et de reconnaître les objets en mouvement (généralement des voitures et des piétons) et de comprendre leur dynamique. Un des défis parmi les plus difficile à résoudre est d’analyser des séquences vidéo dont le nombre d’images par seconde est très faible. Ce type de situation est pourtant fréquent considérant qu’il est très difficile (voir impossible) de transmettre et de stocker sur un serveur un très grand nombre d’images issues de plusieurs caméras. Dans ce cas, les méthodes issues de l’état de l’art échouent car un faible nombre d’images par seconde ne permet pas d’extraire les caractéristiques vidéos utilisées par ces méthodes tels le flux optique, la détection de mouvement et le suivi de véhicules. Au cours de cette thèse, nous nous sommes concentré sur l’analyse de trafic routier à partir de séquences vidéo contenant un très faible nombre d’images par seconde. Plus particulièrement, nous nous sommes concentrés sur les problème d’estimation de la densité du trafic routier et de la classification de véhicules. Pour ce faire, nous avons proposé différents modèles à base de réseaux de neurones profonds (plus particulièrement des réseaux à convolution) ainsi que de nouvelles bases de données permettant d’entraîner les dits modèles. Parmi ces bases de données figure « MIO-TCD », la plus grosse base de données annotées au monde faite pour l’analyse de trafic routier.
| Identifer | oai:union.ndltd.org:usherbrooke.ca/oai:savoirs.usherbrooke.ca:11143/11854 |
| Date | January 2017 |
| Creators | Luo, Zhiming |
| Contributors | Jodoin, Pierre-Marc, Li, Shaozi |
| Publisher | Université de Sherbrooke |
| Source Sets | Université de Sherbrooke |
| Language | English |
| Detected Language | English |
| Type | Thèse |
| Rights | © Zhiming Luo, Attribution - Pas d’Utilisation Commerciale - Pas de Modification 2.5 Canada, Attribution - Pas de Modification 2.5 Canada, http://creativecommons.org/licenses/by-nd/2.5/ca/ |
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