Search results for "Object Detection"
showing 10 items of 64 documents
FastSLAM 2.0: Least-Squares Approach
2006
In this paper, we present a set of robust and efficient algorithms with O(N) cost for the following situations: object detection with a laser ranger; mobile robot pose estimation and a FastSLAM improved implementation. Objected detection is mainly based on a novel multiple line fitting method, related with walls at the environment. This method assumes that walls at the environment constitute a regular constrained angles. A line-based pose estimation method is also proposed, based on Least-Squares (LS). This method performs the matching of detected lines and estimated map lines and it can provide the global pose estimation under assumption of known Data-Association. FastSLAM 1.0 has been imp…
Combining Top-down and Bottom-up Visual Saliency for Firearms Localization
2014
Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the people’s face position. This model has been created by analyzi…
Phase Fourier vector model for scale invariant three-dimensional image detection.
2009
A scale invariant 3D object detection method based on phase Fourier transform (PhFT) is addressed. Three-dimensionality is expressed in terms of range images. The PhFT of a range image gives information about the orientations of the surfaces in the 3D object. When the object is scaled, the PhFT becomes a distribution multiplied by a constant factor which is related to the scale factor. Then 3D scale invariant detection can be solved as illumination invariant detection process. Several correlation operations based on vector space representation are applied. Results show the tolerance of detection method to scale besides discrimination against false objects.
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
2020
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino…
Online Multi-Person Tracking by Tracker Hierarchy
2012
Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers' initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian t…
Object tracking in medical imaging using a 2D active mesh system
2003
International audience; Abstract: This article proposes a technique for tracking moving organs in medical imaging. It can be split into two stages. We first initialize a 2D-triangular mesh on the first image of the sequence. We distinguish different objects of interest by grouping together the triangles that make them up. Afterwards, we deform this mesh on the successive images in order to track each identified object. The tracking stage uses optical flow by adding a node relaxation step to avoid mesh deteriorations. The mesh deformations analysis provides access to motion information along the sequence. This technique is applied to a cine-MRI sequences of the heart and allows the analysis …
Real-Time Hand Pose Recognition Based on a Neural Network Using Microsoft Kinect
2013
The Microsoft Kinect sensor is largely used to detect and recognize body gestures and layout with enough reliability, accuracy and precision in a quite simple way. However, the pretty low resolution of the optical sensors does not allow the device to detect gestures of body parts, such as the fingers of a hand, with the same straightforwardness. Given the clear application of this technology to the field of the user interaction within immersive multimedia environments, there is the actual need to have a reliable and effective method to detect the pose of some body parts. In this paper we propose a method based on a neural network to detect in real time the hand pose, to recognize whether it…
Hyperspectral detection of citrus damage with Mahalanobis kernel classifier
2007
Presented is a full computer vision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.
A simple and efficient face detection algorithm for video database applications
2000
The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences. Peer Reviewed
Context-Aware Model Applied to Hog Descriptor for People Detection
2018
International audience; This work proposes and implements a method based on Context-Aware Visual Attention Model (CAVAM), but modifying the method in such way that the detection algorithm is replaced by Histograms of Oriented Gradients (HOG). After reviewing different algorithms for people detection, we select HOG method because it is a very well known algorithm, which is used as a reference in virtually all current research studies about automatic detection. In addition, it produces accurate results in significantly less time than many algorithms. In this way, we show that CAVAM model can be adapted to other methods for object detection besides Scale-Invariant Feature Transform (SIFT), as …