Modular input processing scheme for object detection using computer vision in intelligent transportations

Author(s):  
Yan Liu ◽  
Wei Zhang ◽  
Shuwen Pan ◽  
Yanjun Li ◽  
Xuejie Wang ◽  
...  
2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.

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


Author(s):  
Muhammad Lanang Afkaar Ar ◽  
Sulthan Muzakki Adytia S ◽  
Yudhistira Nugraha ◽  
Farizah Rizka R ◽  
Andy Ernesto ◽  
...  

2021 ◽  
Vol 11 (23) ◽  
pp. 11241
Author(s):  
Ling Li ◽  
Fei Xue ◽  
Dong Liang ◽  
Xiaofei Chen

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.


2018 ◽  
Vol 3 (01) ◽  
Author(s):  
R.B Wahyu ◽  
Hidayat Saputra

<p>Nowadays going through other places is easier to do. Explore new area is quite common activities by every people since they have sense to know about the environment or situation around them or just curiosity about somewhere else they didn’t know yet. People have different ability to adapt and ways to know how the environment around them. Not all people feeling easy with their new environment and socialize with local people.This research intends to implement object detection as one of feature from computer vision in android application. This application also will assist users how to training image for cascade classifier. User will be able to do object detection using built in Android smartphone camera to receive direction to some place or building from provided marker or logo. It also gives user information about the current place.<em></em></p>


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sultan Daud Khan ◽  
Ahmed B. Altamimi ◽  
Mohib Ullah ◽  
Habib Ullah ◽  
Faouzi Alaya Cheikh

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.


2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.


Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.


2018 ◽  
Vol 155 ◽  
pp. 01016 ◽  
Author(s):  
Cuong Nguyen The ◽  
Dmitry Shashev

Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.


2020 ◽  
Vol 63 (6) ◽  
pp. 1969-1980
Author(s):  
Ali Hamidisepehr ◽  
Seyed V. Mirnezami ◽  
Jason K. Ward

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.


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