scholarly journals Abandoned Object Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 4541-4545
Author(s):  
Anusha Ravi
Keyword(s):  
Object Detection ◽  
Fixed Interval ◽  
Blob Analysis ◽  
Abandoned Object ◽  
Wide Range ◽  
Public Areas ◽  
Great Harm

In the current fast paced life, security has become an important aspect which cannot be ignored. One of the usually neglected but important division of security in public areas involves the unattended objects. Bombs and other harmful objects that are incendiary cause great harm. And in most of the cases such objects are left unattended for some time before causing harm. As such, unattended objects should be given importance and must be checked to ensure safety. This paper shows a method to identify unattended objects in public areas. This is done by comparing a fixed/initial background frame with frames after a fixed interval. If there is a change in the frames, then that refers to an attended object. No change observed, proves an unattended object which is highlighted by a box. This is done with the help of blob analysis. This paper does this with the help of MATLAB. MATLAB being a software accessible to wide range of people provides an easy process with moderate results. It uses a wide range of predefined functions and toolboxes to do this.

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

scholarly journals Object Detection and Sorting using IoT

2014 ◽  
Vol 3 (04) ◽  
pp. 01-04
Author(s):  
Mr. Dharmesh Dhabliya, Ms. Ritika Dhabalia
Keyword(s):  
Internet Of Things ◽  
Object Detection ◽  
Conventional Method ◽  
Industrial Internet Of Things ◽  
Huge Number ◽  
Recycling Industry ◽  
Color Sensor ◽  
Industrial Internet ◽  
Wide Range ◽  
Object Sorting

Color based object sorting has a significant impact in food and processing Industries. Hand picking process in sorting the huge number of objects in industry is very common and laborious task, and time consuming as well, which needs many labors and this conventional method is prone to error. The proposed work aims to replace the hand-picking process by Industrial Internet of Things. The goal of the technique is to sort and count the objects in to different bins accord to their color. A Color sensor, TCS 230 will identify the object and with the help of motors they are made to drop into different bins. The identification of the object is made with the help of frequency concept. As it known that different colors have different wave lengths, so are the different frequencies (f=c/λ). For each frequency, the motor rotates to different angles and thus container is attached to motor is also made to rotate to a certain angle, and the object is made to drop into the bin by a jerk. This action details regarding number of objects manufactured are sent to the IoT server, where the vendor and customer will know the details remotely. This proposed work finds a wide range of usage in fruit industry (to pick the unripen fruit), in candy industry, in grain industry (to remove the black stones from the grains), in recycling industry. 

Abandoned Object Detection on Controlled Scenes Using Kinect

2013 ◽  
pp. 169-178 ◽  
Author(s):  
Antonio Collazos ◽  
David Fernández-López ◽  
Antonio S. Montemayor ◽  
Juan José Pantrigo ◽  
María Luisa Delgado
Keyword(s):  
Object Detection ◽  
Abandoned Object

scholarly journals Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3341 ◽  
Author(s):  
Hilal Tayara ◽  
Kil Chong
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Computation Time ◽  
Aerial Images ◽  
Feature Maps ◽  
Two Stage ◽  
One Stage ◽  
Wide Range ◽  
Feature Pyramid ◽  
Very High

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.

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