object detection
Robust real-time object detection
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This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the Integral
Multiple instance boosting for object detection
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A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of
Contextual models for object detection using boosted random fields
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We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF)
Performance evaluation of object detection and tracking systems
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This paper presents a set of metrics and algorithms for performance evaluation of object tracking systems. Our emphasis is on wide-ranging, robust metrics which can be used for evaluation purposes without inducing any bias towards the evaluation results. The goal is to
A survey on object detection and tracking methods
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The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in sequence of images. Object
Segmentation driven object detection with fisher vectors
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We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent
Voting for Voting in Online Point Cloud Object Detection .
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This paper proposes an efficient and effective scheme to applying the sliding window approach popular in computer vision to 3D data. Specifically, the sparse nature of the problem is exploited via a voting scheme to enable a search through all putative object
A 3D time of flight camera for object detection
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The knowledge of three-dimensional data is essential for many control and navigation applications. Especially in the industrial and automotive environment a fast and reliable acquisition of 3D data has become a main requirement for future developments. Moreover Lighting. When an object is in bright light, it looks brighter than when its in shadow, so a program cant just look at image intensity values. Within-class variation. Different instances of the same kind of object can look quite different to one another. For example, a green station wagon and When a visual observer moves forward the projections of the objects in the scene will move over the visual image. If an object extends vertically from the ground its image will move differently from the immediate background. This difference is called motion parallax [ 2]
Extended set of local binary patterns for rapid object detection
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The paper presents two new encoding schemes for representation of the intensity function in a local neighborhood. The encoding produces binary codes, which are complementary to the standard local binary patterns (LBPs). Both new schemes preserve an important property
Genetic programming for object detection
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This paper examines genetic programming as a machine learning technique in the context of object detection . Object detection is performed on image features and on gray-scale images themselves, with different goals. The generality of the solutions discovered, over the
Transfer learning by borrowing examples for multiclass object detection
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Despite the recent trend of increasingly large datasets for object detection , there still exist many classes with few training examples. To overcome this lack of training data for certain classes, we propose a novel way of augmenting the training data for each class by
Object detection grammars.
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We formulate a general gram model motivated by the problem of object detection in computer vision. We focus on four aspects of modeling objects for the purpose of object detection . First, we are interested in modeling objects as having parts which are themselves
Optical flow based moving object detection and tracking for traffic surveillance
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Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting moving objects in digital videos. The moving detection and tracking system is
Local background enclosure for RGB-D salient object detection
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Recent work in salient object detection has considered the incorporation of depth cues from RGB-D images. In most cases, depth contrast is used as the main feature. However, areas of high contrast in background regions cause false positives for such methods, as the
Techniques for object recognition in images and multi- object detection
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The modern world is enclosed with gigantic masses of digital visual information. Increase in the images has urged for the development of robust and efficient object recognition techniques. Most work reported in the literature focuses on competent techniques for object
Fast object detection using MLP and FFT
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We propose a new technique that speeds up significantly the time needed by a trained network (MLP in our case) to detect a face in a large image. We reformulate neural activities in the hidden layer of the MLP in terms of filter convolution enabling the use of Fourier
Implicit shape models for object detection in 3D point clouds
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We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires
Object detection using Haar-cascade Classifier
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Object detection is an important feature of computer science. The benefits of object detection is however not limited to someone with a doctorate of informatics. Instead, object detection is growing deeper and deeper into the common parts of the information society, lending a helping
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