Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 363 for “"object detection"”.
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Fast object detection
… are composite chunks of meaning. We show that object detectors could be better at detecting some visual phrases than detecting single objects. This process of image understanding needs to use a lot of detectors. Running conventional object detectors at the rate required for image understanding …
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Visualizing object detection features
… algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and …
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3D Object Detection from Images
… Such actions require to identify and localize objects in the environment, effectively building a robust understanding of the scene. Humans easily gain this understanding thanks to their binocular vision, which provides an high-resolution and continuous stream of information to our brain that …
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Learning Object Detection with Weak Supervision
… datasets are laborious, especially for object detection --- a challenging vision task. A promising solution for reducing costs is to train models with weak supervision, which provides a good trade-off between model performance and annotation efficiency. This thesis dedicates to weakly …
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Object Detection Using Vision Transformed EfficientDet
This research presents a novel approach for object detection by integrating Vision Transformers (ViT) into the EfficientDet architecture. The field of computer vision, encompassing artificial intelligence, focuses on the interpretation and analysis of visual data. Recent advancements in deep …
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Reducing false positives for object detection
… a large negative impact on the performance of object detectors. We conjecture three factors that lie behind hard false positives, and we confirm the conjecture with experiments that prove the following: (1) Shared feature representation is not optimal due to the mismatched goals of feature …
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Probabilistic visual learning for object detection
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Dropped object detection in crowded scenes
… we consider the problem of detecting abandoned objects in a crowded scene. Assuming that the scene is being captured through a mid-field static camera, our approach consists of segmenting the foreground from the background and then using a change analyzer to detect any objects which meet certain …
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Object Detection and Tracking Using Uncalibrated Cameras
This thesis considers the problem of tracking an object in world coordinates using measurements obtained from multiple uncalibrated cameras. A general approach to track the location of a target involves different phases including calibrating the camera, detecting the object's feature points over …
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REAL-TIME OBJECT DETECTION FOR AUTONOMOUS DRIVING
A deep dive into deep learning object detection and optimizing the algorithms for autonomous driving application using channel pruning.
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Autonomous real-time object detection and identification
… power available for processing. The objective of this work is to detect and identify objects in real-time, with a low power footprint so that it can operate on a UAV. An appraisal of current computer vision methods is presented, with reference to their performance and applicability to …
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Improving Object Detection using Enhanced EfficientNet Architecture
… in EfficientNet on transfer learning and object detection tasks, where it achieves higher accuracy with fewer parameters and less computation. Henceforward, the proposed enhanced architecture will be discussed in detail and compared to the original architecture. Our approach provides a …
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Hierarchical transfer learning for small object detection
Object detection faces a formidable challenge in detecting small objects due to their diminutive size and sparse pixel representation. This difficulty is exacerbated by the common lack of resolution in images, making the task of distinguishing small objects complex. Annotating data for training …
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Uncertainty Estimation for Single Stage Object Detection
… networks deliver state-of-the-art performance in object detection, their inherent tendency toward overconfidence compromises their reliability in safety-critical applications, necessitating robust methods for uncertainty quantification. Although full Bayesian inference would provide the most …
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Learning Models for Multi-Viewpoint Object Detection
… over the locations and visibility of all the object parts. The second approach employs a discriminative Conditional Random Field based model to encode the relative geometry and co-occurrence constraints.
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Current and future trends in object detection
The task of finding objects belonging to classes of interest in images has long been a focus of Computer Vision research. The ability to localize objects is useful in many applications: from self-driving cars, where it allows the car to detect pedestrians, bicyclists, road signs, and other …
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Robust object detection in images by components
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Depth sensor based object detection using surface curvature
An object detection system finds objects from an image or video sequence of the real world. The good performance of object detection has been largely driven by the development of well-established robust feature sets. By using conventional color images as input, researchers have achieved major …
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