Improving Object Detection in Real-World Traffic Scenes

2021 ◽  
pp. 288-299
Author(s):  
Waqar Khan ◽  
Fang Liu ◽  
Marta Vos
Keyword(s):  
Object Detection ◽  
Real World

Improving Real-world Object Detection Using Balanced Loss

2020 ◽  
Author(s):  
Shengyang Shen ◽  
Zexiang Liu ◽  
Bingkun Zhao ◽  
Li Chen ◽  
Chongyang Zhang
Keyword(s):  
Object Detection ◽  
Real World

Midground object detection in real world video scenes

2007 ◽  
Author(s):  
B. Valentine ◽  
S. Apewokin ◽  
L. Wills ◽  
S. Wills ◽  
A. Gentile
Keyword(s):  
Object Detection ◽  
Real World

scholarly journals A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 1774-1778
Author(s):  
Mahesh Singh
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Real World ◽  
Research Paper ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Great Success ◽  
The Real ◽  
Road Traffic Safety ◽  
Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

scholarly journals TCM: Temporal Consistency Model for Head Detection in Complex Videos

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sultan Daud Khan ◽  
Ahmed B. Altamimi ◽  
Mohib Ullah ◽  
Habib Ullah ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Real World ◽  
Experimental Evaluation ◽  
Research Problem ◽  
Temporal Consistency ◽  
Single Image ◽  
Average Precision ◽  
Consistency Model ◽  
Head Detection

Head detection in real-world videos is a classical research problem in computer vision. Head detection in videos is challenging than in a single image due to many nuisances that are commonly observed in natural videos, including arbitrary poses, appearances, and scales. Generally, head detection is treated as a particular case of object detection in a single image. However, the performance of object detectors deteriorates in unconstrained videos. In this paper, we propose a temporal consistency model (TCM) to enhance the performance of a generic object detector by integrating spatial-temporal information that exists among subsequent frames of a particular video. Generally, our model takes detection from a generic detector as input and improves mean average precision (mAP) by recovering missed detection and suppressing false positives. We compare and evaluate the proposed framework on four challenging datasets, i.e., HollywoodHeads, Casablanca, BOSS, and PAMELA. Experimental evaluation shows that the performance is improved by employing the proposed TCM model. We demonstrate both qualitatively and quantitatively that our proposed framework obtains significant improvements over other methods.

scholarly journals Boundary and Object Detection in Real World Images

1976 ◽  
Vol 23 (4) ◽  
pp. 599-618 ◽  
Author(s):  
Yoram Yakimovsky
Keyword(s):  
Object Detection ◽  
Real World

scholarly journals How do targets, nontargets, and scene context influence real-world object detection?

2017 ◽  
Vol 79 (7) ◽  
pp. 2021-2036 ◽  
Author(s):  
Harish Katti ◽  
Marius V. Peelen ◽  
S. P. Arun
Keyword(s):  
Object Detection ◽  
Real World ◽  
Scene Context

Understanding Evolved Genetic Programs for a Real World Object Detection Problem

2005 ◽  
pp. 351-360 ◽  
Author(s):  
Victor Ciesielski ◽  
Andrew Innes ◽  
Sabu John ◽  
John Mamutil
Keyword(s):  
Object Detection ◽  
Real World ◽  
Detection Problem

scholarly journals Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5323
Author(s):  
Yongsu Kim ◽  
Hyoeun Kang ◽  
Naufal Suryanto ◽  
Harashta Tatimma Tatimma Larasati ◽  
Afifatul Mukaroh ◽  
...  
Keyword(s):  
Object Detection ◽  
Real World ◽  
Deep Neural Networks ◽  
Target Object ◽  
Human Vision ◽  
Target System ◽  
Detection Model ◽  
Detection Systems ◽  
Attack Scenario ◽  
Representative Area

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.

scholarly journals End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy

Soft Matter ◽  
2020 ◽  
Vol 16 (7) ◽  
pp. 1751-1759 ◽  
Author(s):  
Eric N. Minor ◽  
Stian D. Howard ◽  
Adam A. S. Green ◽  
Matthew A. Glaser ◽  
Cheol S. Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real World ◽  
Simulated Data ◽  
Video Microscopy ◽  
Experimental Physics ◽  
Simulated Images

We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data.

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