Cristiano Premebida

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Why is pedestrian detection still very challenging in realistic scenes? How much would a successful solution to monocular depth inference aid pedestrian detection? In order to answer these questions we trained a state-of-the-art deformable parts detector using different configurations of optical images and their associated 3D point clouds, in conjunction(More)
In this report some algorithms for 2D segmentation, feature detection and fitting are presented. The features discussed here consist of three geometric primitives: lines, circles and ellipses. The segmentation process, whose objective is grouping segments that belong to the same object, is analysed using several kinds of algorithms. Results are presented(More)
In this work, we propose an approach that relies on cues from depth perception from RGB-D images, where features related to human body motion (3D skeleton features) are used on multiple learning classifiers in order to recognize human activities on a benchmark dataset. A Dynamic Bayesian Mixture Model (DBMM) is designed to combine multiple classifier(More)
Intelligent vehicles need reliable information about the environment in order to operate with total safety. In this paper we propose a flexible multi-module architecture for a multi-target detection and tracking system (MTDTS) complemented with a Bayesian object classification layer based on finite Gaussian mixture models (GMM). The GMM parameters are(More)
A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for(More)
This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object(More)
In this work, a context-based multisensor system, applied for pedestrian detection in urban environment, is presented. The proposed system comprises three main processing modules: (i) a LIDAR-based module acting as primary object detection, (ii) a module which supplies the system with contextual information obtained from a semantic map of the roads, and(More)
Pedestrian detection systems constitute an important field of research and development in computer vision, specially when applied in protection/safety systems in urban scenarios due to their direct impact in the society, specifically in terms of traffic casualties. In order to face such challenge, this work exploits some developments on statistical machine(More)