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Object detection

About: Object detection is a(n) research topic. Over the lifetime, 46185 publication(s) have been published within this topic receiving 1352573 citation(s).

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Papers
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Journal ArticleDOI: 10.1038/NATURE14539
Yann LeCun1, Yann LeCun2, Yoshua Bengio3, Geoffrey E. Hinton4  +1 moreInstitutions (5)
28 May 2015-Nature
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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33,931 Citations


Open accessProceedings ArticleDOI: 10.1109/CVPR.2005.177
Navneet Dalal1, Bill Triggs1Institutions (1)
20 Jun 2005-
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

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Topics: Histogram of oriented gradients (62%), Local binary patterns (57%), GLOH (56%) ...read more

28,803 Citations


Open accessJournal ArticleDOI: 10.1007/S11263-015-0816-Y
Olga Russakovsky1, Jia Deng2, Hao Su1, Jonathan Krause1  +8 moreInstitutions (4)
Abstract: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

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25,260 Citations


Open accessPosted Content
Shaoqing Ren1, Kaiming He2, Ross Girshick3, Jian Sun2Institutions (3)
Abstract: State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

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Topics: Object detection (58%)

23,121 Citations


Open accessBook ChapterDOI: 10.1007/978-3-319-10602-1_48
Tsung-Yi Lin1, Michael Maire2, Serge Belongie1, James Hays  +4 moreInstitutions (4)
06 Sep 2014-
Abstract: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

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  • Fig. 1: While previous object recognition datasets have focused on (a) image classification, (b) object bounding box localization or (c) semantic pixel-level segmentation, we focus on (d) segmenting individual object instances. We introduce a large, richly-annotated dataset comprised of images depicting complex everyday scenes of common objects in their natural context.
    Fig. 1: While previous object recognition datasets have focused on (a) image classification, (b) object bounding box localization or (c) semantic pixel-level segmentation, we focus on (d) segmenting individual object instances. We introduce a large, richly-annotated dataset comprised of images depicting complex everyday scenes of common objects in their natural context.
  • Fig. 10: User interfaces for non-iconic image collection. (a) Interface for selecting non-iconic images containing pairs of objects. (b) Interface for selecting non-iconic images for categories that rarely co-occurred with others.
    Fig. 10: User interfaces for non-iconic image collection. (a) Interface for selecting non-iconic images containing pairs of objects. (b) Interface for selecting non-iconic images for categories that rarely co-occurred with others.
  • Fig. 14: Random chair instances from PASCAL VOC and MS COCO. At most one instance is sampled per image.
    Fig. 14: Random chair instances from PASCAL VOC and MS COCO. At most one instance is sampled per image.
  • TABLE 3: Scene category list.
    TABLE 3: Scene category list.
  • Fig. 15: Examples of borderline segmentations that passed (top) or were rejected (bottom) in the verification stage.
    Fig. 15: Examples of borderline segmentations that passed (top) or were rejected (bottom) in the verification stage.
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Topics: Object detection (54%)

18,843 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202276
20214,799
20205,257
20194,545
20183,351
20172,481

Top Attributes

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Topic's top 5 most impactful authors

Luc Van Gool

52 papers, 16.7K citations

Mubarak Shah

47 papers, 11.6K citations

Alan L. Yuille

42 papers, 9.9K citations

Larry S. Davis

38 papers, 3.7K citations

Mohan M. Trivedi

33 papers, 1.5K citations

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