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In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
Multiple sensor systems are extremely used in autonomous driving for providing increased object detection accuracy. We present a multiple sensor based pedestrian detection system that combines aggregated channel features classifiers trained on images captured with two types of sensors: far infrared and stereovision sensors.
Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined scales. We port this idea to the infrared...
We propose a method for detecting pedestrians in infrared images. The method combines a fast region of interest generator with fast feature pyramid object detection. Knowing the appearance model of pedestrians in infrared images we infer some edge and intensity based filters that generate the regions in which pedestrian hypotheses may appear. On those regions we apply the Aggregated Channel Features...
In the automotive industry the issue of safety remains a major priority. This aspect is not focused just on the driver but also on the other participants of the traffic like the pedestrians. This paper describes a pedestrian detection system where three different classification methods are used for detecting pedestrians with a far infrared camera. The three methods are tested and compared on variable...
Most of computation time when dealing with a pedestrian detector is spent in the feature computation and then in the multi-scale classification. This second step consists of applying scanning windows at multiple scales. Depending on the number of scales and on the image dimension, this step is slow because a large number of windows is generated. An efficient pruning algorithm able to remove most of...
We propose a novel algorithm that detects pedestrians based on their body appearance. As a pedestrian has a high variance in shape we create a star based classification scheme that contains a cascaded root classifier (trained on multiple attitudes) and four classifiers trained on specific pedestrian attitudes (rear, front, lateral left and lateral right). We use Histogram of Oriented Gradient features...
Object recognition is an essential task in content-based image retrieval and classification. This paper deals with object recognition in WIKImage data, a collection of publicly available annotated Wikipedia images. WIKImage comprises a set of 14 binary classification problems with significant class imbalance. Our approach is based on using the local invariant image features and we have compared 3...
Accurate pedestrian detection in urban environment is a highly explored research field. We propose a new approach in pedestrian detection that combines the popular Local Binary Patterns and Histogram of Oriented Gradient features. The novelty of our work resides in the combination of a reduced HOG feature vector with uniform LBP patterns for the pedestrian data representation. Another contribution...
We present several methods of pedestrian detection in intensity images using different local statistical measures applied to two classes of features extensively used in pedestrian detection: uniform local binary patterns — LBP and a modified version of histogram of oriented gradients — HOG. Our work extracts local binary patterns and magnitude and orientation of the gradient image. Then we divide...
This paper describes a new approach for pedestrian detection in traffic scenes. The originality of the method resides in the combination of the benefits of the symmetry characteristic for pedestrians in intensity images and the benefits of deformable part-based models for recognizing pedestrians in multiple object hypotheses generated by a stereo vision system. A mixture model based on several pedestrian...
One central task to the idea of Semantic Web is reasoning over semantic descriptions of web pages and information items available on the Web. A flagship project that is advancing the state of the art in reasoning with Web scale data is the Large Knowledge Collider (LarKC). Having a plug gable architecture, LarKC enables the interested users to test their reasoning approaches with very little overhead...
In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc1 ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification methods based on AdaBoost and Random forest algorithms...
Modeling the performance of large scale systems is the core idea of this paper.We focus on modeling the performance specific behavior of LarKC 1- The Large Knowledge Collider a platform for large scale integrated reasoning and Web-search. A set of instrumentation and monitoring tools are employed to collect metrics related to execution time, resources, and specific platform measurements like running...
One central task to the idea of Semantic Web is reasoning over semantic descriptions of web pages and information items available on the Web. A flagship project that is advancing the state of the art in reasoning with Web-scale data is the Large Knowledge Collider (LarKC). Having a pluggable architecture, LarKC enables the interested users to test their reasoning approaches with very little overhead...
The bag of words model has been actively adopted by content based image retrieval and image annotation techniques. We employ this model for the particular task of pedestrian detection in two dimensional images, producing this way a novel approach to pedestrian detection. The experiments we have done in this paper compare the behavior of discriminative recognition approaches that use AdaBoost on codebook...
Object recognition from images is one of the essential problems in automatic image processing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points...
Reasoning is central to the idea of the Semantic Web and ontologies, however, the fundamental principles of reasoning - soundness and completeness - do not match the reality of the (Semantic) Web that is ruled by contradicting and incomplete data and claims. Furthermore, logical reasoning is strong for rather small numbers of axioms and facts, while the Web is growing at an impressive speed and hence...
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