scholarly journals Improving pedestrian detection using MPEG-7 descriptors

2013 ◽  
Vol 11 ◽  
pp. 101-105 ◽  
Author(s):  
H. Lietz ◽  
M. Ritter ◽  
R. Manthey ◽  
G. Wanielik
Keyword(s):  
Object Detection ◽  
Open Source ◽  
High Performance ◽  
Image Acquisition ◽  
Pedestrian Detection ◽  
Weather Conditions ◽  
Image Features ◽  
Detection Systems ◽  
Naturalistic Driving ◽  
Current Systems

Abstract. During the last decade, modern Pedestrian Detection Systems made massive use of the steadily growing numbers of high-performance image acquisition sensors. Within our naturalistic driving environment, a lot of different and heterogeneous scenes occur that are caused by varying illumination and weather conditions. Unfortunately, current systems do not work properly under these hardened conditions. The aim of this article is to investigate and evaluate observed video scenes from an open source dataset by using various image features in order to create a basis for robust and more accurate object detection.

The Research and Design of Portable Leaf Disease Spots Image Acquisition System

2011 ◽  
Vol 128-129 ◽  
pp. 1480-1483
Author(s):  
Kai Song ◽  
Zhe Wang ◽  
Feng Ju Shen
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Image Acquisition ◽  
Processing System ◽  
Image Features ◽  
Acquisition System ◽  
Ultra Low Power ◽  
Real Time Processing ◽  
Leaf Disease ◽  
Image Acquisition System

The crop leaf disease spots image recognition technology has a great significance in identifying diseases and in effective spraying of pesticides to liminate diseases. We researched and designed the portable leaf disease spots image acquisition system since current PC-based processing system is high cost and lack of flexibility and real-timing, This system uses a high-performance 32-bit DSP chip based DM642 as its main processor, and also uses ultra-low power consumption and ultra-small size video decoder chips, large-capacity memories and high-performance communicator interfaces. As a result of the reasonable design of hardware architecture, this system realized leaf disease spots image features collecting and processing ，video transmitting, and achieved strong real-time processing capability, easy operation, low cost, anti-interference ability and other characteristics. The system prototype works well, and meets the design requirements.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)

2019 ◽  
Vol 17 (1) ◽  
pp. 69-76
Author(s):  
Mohammad Shiddiq Ghozali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Object Detection ◽  
Open Source ◽  
Automated Teller Machine

Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat CCTV akan tetapi penjahat menggunakan alat bantu untuk menutupi wajahnya seperti helm, topi, masker dan kacamata hitam. Biasanya didepan pintu masuk ATM terpampang larangan untuk tidak menggunakan helm, topi, masker dan kacamata hitam serta tidak membawa rokok. Akan tetapi larangan itu masih tetap ada yang melanggar, dikarenakan tidak ada tindak lanjut ketika seseorang menggunakan benda-benda yang dilarang dibawa kedalam ATM. Oleh karena itu penulis membuat sistem pendeteksi obyek di bidang Kecerdasan Buatan untuk mendeteksi benda-benda yang dilarang digunakan ketika berada di ATM. Salah satu metode yang digunakan untuk menciptakan Object Detection yaitu You Only Look Once (YOLO). Implementasi ide ini tersedia pada DARKNET (open source neural network). Cara kerja YOLO yaitu dengan melihat seluruh gambar sekali, kemudian melewati jaringan saraf sekali langsung mendeteksi object yang ada. Oleh karena itu disebut You Only Look Once (YOLO). Pada penelitian ini, penulis membuat sistem yang masih dalam bentuk pengembangan, sehingga menjalankannya masih menggunakan command prompt. Keywords : Automated Teller Machine (ATM), Kecerdasan Buatan, Pendeteksi Obyek, You Only Look Once (YOLO)  

Perception

2018 ◽  
Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio
Keyword(s):  
Computational Neuroscience ◽  
Feature Detection ◽  
Pedestrian Detection ◽  
Detection Systems ◽  
Catching Up ◽  
Feature Detectors ◽  
Recognition Systems ◽  
Engineered Systems ◽  
Perceptual Systems ◽  
The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

scholarly journals Ship Handling in Unprotected Waters: A Review of New Technologies in Escort Tugs to Improve Safety

2021 ◽  
Vol 2 (1) ◽  
pp. 46-62
Author(s):  
Santiago Iglesias-Baniela ◽  
Juan Vinagre-Ríos ◽  
José M. Pérez-Canosa
Keyword(s):  
High Performance ◽  
New Technologies ◽  
Weather Conditions ◽  
Hull Form ◽  
Best Available Technologies ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
New Type ◽  
Technological Challenge ◽  
And Performance

It is a well-known fact that the 1989 Exxon Valdez disaster caused the escort towing of laden tankers in many coastal areas of the world to become compulsory. In order to implement a new type of escort towing, specially designed to be employed in very adverse weather conditions, considerable changes in the hull form of escort tugs had to be made to improve their stability and performance. Since traditional winch and ropes technologies were only effective in calm waters, tugs had to be fitted with new devices. These improvements allowed the remodeled tugs to counterbalance the strong forces generated by the maneuvers in open waters. The aim of this paper is to perform a comprehensive literature review of the new high-performance automatic dynamic winches. Furthermore, a thorough analysis of the best available technologies regarding towline, essential to properly exploit the new winches, will be carried out. Through this review, the way in which the escort towing industry has faced this technological challenge is shown.

An open source high-performance solution to extract surface water drainage networks from diverse terrain conditions

2017 ◽  
Vol 45 (4) ◽  
pp. 319-328 ◽  
Author(s):  
Lawrence V. Stanislawski ◽  
Kornelijus Survila ◽  
Jeffrey Wendel ◽  
Yan Liu ◽  
Barbara P. Buttenfield
Keyword(s):  
Surface Water ◽  
Open Source ◽  
High Performance ◽  
Water Drainage ◽  
Drainage Networks

Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

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