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This work presents an original real time, robust micro-expression detection algorithm. The algorithm analyses the movement modifications that occur around the most prominent facial regions using two absolute frame differences. Next, a machine learning algorithm is used to predict if a micro-expression occurred at a given frame t. Two classifiers were evaluated: decision tree and random forest classifier...
The environment representation is one of the main challenges of autonomous navigation. In the case of complex driving environments such as crowded city traffic scenarios, achieving satisfactory results becomes even more difficult. In this paper we propose a real-time solution for two main issues of advanced driver assistance systems: unstructured environment representation and extraction of dynamic...
This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach...
For a Driving Assistance System dedicated to intersection safety, knowledge about the structure and position of the intersection is essential, and detecting the painted road signs can greatly improve this knowledge. This paper describes a method for detection, measurement and classification of painted road objects that are typically found in European intersections. The features of the painted objects...
Alignment of the stereo perception outcomes with the GPS based digital map information is the main goal of this paper. This alignment relies on discriminative sensorial landmarks as stop lines or lane markings and augmented GPS maps. The alignment is needed to provide: an increased accuracy global localization of the static and dynamic detected entities; fusion of the perceived sensorial information...
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