2,753 research outputs found
Distance to Center of Mass Encoding for Instance Segmentation
The instance segmentation can be considered an extension of the object
detection problem where bounding boxes are replaced by object contours.
Strictly speaking the problem requires to identify each pixel instance and
class independently of the artifice used for this mean. The advantage of
instance segmentation over the usual object detection lies in the precise
delineation of objects improving object localization. Additionally, object
contours allow the evaluation of partial occlusion with basic image processing
algorithms. This work approaches the instance segmentation problem as an
annotation problem and presents a novel technique to encode and decode ground
truth annotations. We propose a mathematical representation of instances that
any deep semantic segmentation model can learn and generalize. Each individual
instance is represented by a center of mass and a field of vectors pointing to
it. This encoding technique has been denominated Distance to Center of Mass
Encoding (DCME)
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges
Vehicle re-identification (ReID) endeavors to associate vehicle images
collected from a distributed network of cameras spanning diverse traffic
environments. This task assumes paramount importance within the spectrum of
vehicle-centric technologies, playing a pivotal role in deploying Intelligent
Transportation Systems (ITS) and advancing smart city initiatives. Rapid
advancements in deep learning have significantly propelled the evolution of
vehicle ReID technologies in recent years. Consequently, undertaking a
comprehensive survey of methodologies centered on deep learning for vehicle
re-identification has become imperative and inescapable. This paper extensively
explores deep learning techniques applied to vehicle ReID. It outlines the
categorization of these methods, encompassing supervised and unsupervised
approaches, delves into existing research within these categories, introduces
datasets and evaluation criteria, and delineates forthcoming challenges and
potential research directions. This comprehensive assessment examines the
landscape of deep learning in vehicle ReID and establishes a foundation and
starting point for future works. It aims to serve as a complete reference by
highlighting challenges and emerging trends, fostering advancements and
applications in vehicle ReID utilizing deep learning models
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
A Comparative Study on Effects of Original and Pseudo Labels for Weakly Supervised Learning for Car Localization Problem
In this study, the effects of different class labels created as a result of
multiple conceptual meanings on localization using Weakly Supervised Learning
presented on Car Dataset. In addition, the generated labels are included in the
comparison, and the solution turned into Unsupervised Learning. This paper
investigates multiple setups for car localization in the images with other
approaches rather than Supervised Learning. To predict localization labels,
Class Activation Mapping (CAM) is implemented and from the results, the
bounding boxes are extracted by using morphological edge detection. Besides the
original class labels, generated class labels also employed to train CAM on
which turn to a solution to Unsupervised Learning example. In the experiments,
we first analyze the effects of class labels in Weakly Supervised localization
on the Compcars dataset. We then show that the proposed Unsupervised approach
outperforms the Weakly Supervised method in this particular dataset by
approximately %6.Comment: 8 pages, 5 figure
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