Efficient final output feature map processing method supporting real-time object detection and recognition

2020 ◽  
Author(s):  
Seong Bin Choi ◽  
Sang-Seol Lee ◽  
Jonghee Park ◽  
Sung-Joon Jang ◽  
Byung-Ho Choi
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Processing Method ◽  
Feature Map ◽  
Map Processing ◽  
Final Output ◽  
Detection And Recognition

PERFORMANCE MEASUREMENT OF REAL TIME OBJECT DETECTION AND RECOGNITION

2007 ◽  
Author(s):  
MIR MD. JAHANGIR KABIR ◽  
SAMIR HALDER ◽  
MD. ROBIUR RAHMAN ◽  
MD. W. H. SADID ◽  
M. M. MANJURUL ISLAM ◽  
...  
Keyword(s):  
Object Detection ◽  
Performance Measurement ◽  
Real Time ◽  
Detection And Recognition

A Robust Real-Time Object Detection and Recognition Algorithm for Multiple Objects

2019 ◽  
Vol 12 ◽  
Author(s):  
Garv Modwel ◽  
Anu Mehra ◽  
Nitin Rakesh ◽  
K K Mishra
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hybrid Method ◽  
Hybrid Algorithm ◽  
Time Lag ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Real Time System ◽  
Real Time Processing ◽  
Detection And Recognition

Background: Object detection algorithm scans every frame in the video to detect the objects present which is time consuming. This process becomes undesirable while dealing with real time system, which needs to act with in a predefined time constraint. To have quick response we need reliable detection and recognition for objects. Methods: To deal with the above problem a hybrid method is being implemented. This hybrid method combines three important algorithms to reduce scanning task for every frame. Recursive Density Estimation (RDE) algorithm decides which frame need to be scanned. You Look at Once (YOLO) algorithm does the detection and recognition in the selected frame. Detected objects are being tracked through Speed-up Robust Feature (SURF) algorithm to track the objects in subsequent frames. Results: Through the experimental study, we demonstrate that hybrid algorithm is more efficient compared to two different algorithm of same level. The algorithm is having high accuracy and low time latency (which is necessary for real time processing). Conclusion: The hybrid algorithm is able to detect with a minimum accuracy of 97 percent for all the conducted experiments and time lag experienced is also negligible, which makes it considerably efficient for real time application.

Real Time Object Detection and Recognition

2021 ◽  
Author(s):  
Satyanarayan Pandey ◽  
S.P. Ramesh ◽  
Himanshu . ◽  
Ashutosh Singh
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection And Recognition

scholarly journals Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 260 ◽  
Author(s):  
Kevin Kevin ◽  
Nico Gunawan ◽  
Mariana Erfan Kristiani Zagoto ◽  
Laurentius Laurentius ◽  
Amir Mahmud Husein
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Video Quality ◽  
Vehicle Detection ◽  
Neural Network Method ◽  
Network Method ◽  
Object Of Study ◽  
Capture Capacity ◽  
Detection And Recognition

Abstract— The purpose of this study is to compare the video quality between the Samsung HP camera and the Xiaomi HP camera. The object of study was UNPRI students who walked through the front yard of the UNPRI SEKIP campus. Here we test how accurate the camera's HP capture capacity is used to take the video. The method used to test this research is the Convolution Neural Network method. Object detection and recognition aim to detect and classify objects that can be applied to various fields such as face, human, pedestrian, vehicle detection (Pedoeem & Huang, 2018), besides the ability to find, identify, track and stabilize objects in various poses and important backgrounds in many real-time video applications. Object detection, tracking, alignment and stabilization have become very interesting fields of research in the vision and recognition of computer patterns due to the challenging nature of several slightly different objects such as object detection, where the algorithm must be precise enough to identify, track and center an object from the others

scholarly journals Currency Detection for Visually Impaired Iraqi Banknote as a Study Case

2021 ◽  
Vol 12 (6) ◽  
pp. 2940-2948
Author(s):  
Raghad Raied Mahmood Et al.
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Visually Impaired ◽  
Detection System ◽  
Detection Algorithm ◽  
Geometric Transformations ◽  
Visually Impaired People ◽  
Paper Currency ◽  
Impaired People ◽  
Detection And Recognition

It is relatively simple for a normal human to interpret and understand every banknote, but one of the major problems for visually impaired people are money recognition, especially for paper currency. Since money plays such an important role in our everyday lives and is required for every business transaction, real-time detection and recognition of banknotes become a necessity for blind or visually impaired people For that purpose, we propose a real-time object detection system to help visually impaired people in their daily business transactions. Dataset Images of the Iraqi banknote category are collected in different conditions initially and then, these images are augmented with different geometric transformations, to make the system strong. These augmented images are then annotated manually using the "LabelImg" program, from which training sets and validation image sets are prepared. We will use YOLOv3 real-time Object Detection algorithm trained on custom Iraqi banknote dataset for detection and recognition of banknotes. Then the label of the banknotes is identified and then converted into audio by using Google Text to Speech (gTTS), which will be the expected output. The performance of the trained model is evaluated on a test dataset and real-time live video. The test results demonstrate that the proposed method can detect and recognize Iraqi paper money with high mAP reaches 97.405% and a short time.

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