Abstract-This paper presents a complete method for pedestrian detection applied to infrared images. First, we study an image descriptor based on histograms of oriented gradients (HOG), associated with a Support Vector Machine (SVM) classifier and evaluate its efficiency. After having tuned the HOG descriptor and the classifier, we include this method in a complete system, which deals with stereo infrared images. This approach gives good results for window classification, and a preliminary test applied on a video sequence proves that this approach is very promising.

This paper presents a stereo vision system for the detection and distance computation of a preceding vehicle. It is divided in two major steps. Initially, a stereo vision-based algorithm is used to extract relevant three-dimensional (3-D) features in the scene, these features are investigated further in order to select the ones that belong to vertical objects only and not to the road or background. These 3-D vertical features are then used as a starting point for preceding vehicle detection; by using a symmetry operator, a match against a simplified model of a rear vehicle's shape is performed using a monocular vision-based approach that allows the identification of a preceding vehicle. In addition, using the 3-D information previously extracted, an accurate distance computation is performed.

Abstract-In this paper two different vision based systems for vehicle detection are described and their integration discussed. The first approach is based on the use of a specific model for vehicles and mostly relies on monocular vision. Conversely, the second system is based on the use of stereo vision and allows to refine the coarse results obtained by the former.A preliminary integration of the two systems has been tested on the ARGO experimental vehicle and some remarks about reliability and robustness are also included.

In this paper different matching cost functions used for stereo matching are evaluated in the context of intelligent vehicles applications. Classical costs are considered, like: sum of squared differences, normalized cross correlation or census transform that were already evaluated in previous studies, together with some recent functions that try to enhance the discriminative power of Census Transform (CT). These are evaluated with two different stereo matching algorithms: a global method based on graph cuts and a fast local one based on cross aggregation regions. Furthermore we propose a new cost function that combines the CT and alternatively a variant of CT called Cross-Comparison Census (CCC), with the mean sum of relative pixel intensity differences (DIFFCensus). Among all the tested cost functions, under the same constraints, the proposed DIFFCensus produces the lower error rate on the KITTI road scenes dataset 1 with both global and local stereo matching algorithms.

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Abstract-This paper presents a stereo vision system for vehicle detection. It has been conceived as the integration of two different subsystems. Initially a stereo vision based system is used to recover the most relevant 3D features in the scene; due to the algorithm's generality, all the vertical features are extracted as potentially belonging to a vehicle in front of the vision system. This list of significant patterns is fed to a second subsystem based on monocular vision; it processes the list computing a match with a general model of a vehicle based on symmetry and shape, thus allowing the identification of the sole characteristics belonging to a vehicle.The system presented in this work derives from the integration of the research work developed by the University of Parma (Italy) and I.N.S.A. of Rouen (France). The two subsystems have been integrated into the GOLD software and are currently under testing using the ARGO experimental vehicle.

This paper presents a real-time stereo image sequences matching approach dedicated to intelligent vehicles applications. The main idea of the paper consists in integrating temporal information into the matching scheme. The estimation of the disparity map of an actual frame exploits the disparity map estimated for its preceding frame. An association between the two frames is searched, i.e. temporal integration. The disparity range is inferred for the actual frame based on both the association and the disparity map of the preceding frame. Dynamic programming technique is considered for matching the image features. As a similarity measure, a new cost function is defined. The proposed approach is tested on virtual and real stereo image sequences and the results are satisfactory. The method is fast and able to provide about 20 millions disparity maps per second on a HP Pavilion dv6700 2.1GHZ.

Pedestrian detection is an important but challenging component of an Intelligent Transportation System. In this paper, we describe a pedestrian detection system based on a monocular vision with a Far-Infrared camera (FIR). We propose an original feature representation, called Intensity Self Similarity (ISS) , adapted to pedestrian detection in FIR images. The ISS representation is based on the relative intensity self similarity within a pedestrian region of interest (ROI) hypothesis. Our system consists of two components. The first component generates pedestrian ROI hypothesis by exploiting the specific characteristics of FIR images, where pedestrian shapes may vary in large scale, but heads appear usually as light regions. Pedestrian ROI are detected, with high recall rate, due to a Hierarchical Codebook (HC) of Speeded-Up Robust Features (SURF) located in light head regions. The second component consists of pedestrian hypothesis validation, by using a pedestrian full-body classification based on the ISS representation, with Support Vector Machine (SVM). For classification, we retained two feature descriptors: the Histogram of Oriented Gradients (HOG) descriptor and the original ISS feature representation that we proposed for FIR images. The early fusion of these two features enhances significantly the system precision, attaining an F-measure for the pedestrian class of 97.7%. Moreover, this feature fusion outperforms the state-of-the-art SURF descriptor proposed previously. The experimental evaluation shows that our pedestrian detector is also robust, since it performs well in detecting pedestrians even in large scale and crowded real-world scenes.