scholarly journals An advanced implementation idea for detecting real-time object occupancy

2020 ◽  
Author(s):  
Claremary James ◽  
Varghese James
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Natural Disaster ◽  
Motion Detector ◽  
Human Presence ◽  
Detection Technology ◽  
Occupancy Detection ◽  
Infrared Rays ◽  
Per Se ◽  
Lost Object

Object detection is a title that has earned significances over several fields which have always benefitted socially during circumstances, namely incidents involving human endangerment such as natural disaster where threat may occur in the form of an earthquake, human entrapment underneath rubbles per se. The usage of PIR (Passive Infrared Rays) motion detector to detect humans, objects and other living beings through their movement, has proven the ability in handling situations where such detection is the best chance. However, this approach is not utilized in every situation. In the proposed research paper, an object occupancy detection technology notion is detailed which will describe the function to detect the occupancy or presence of human in an area, specifically transport vehicles that will help in determining passengers inside and to find lost objects as well. The motive behind raising this technological need is to aid or assist in occurrence wherein facing difficulty to find an object being lost or misplaced in a space, as well to detect the humans occupied. This assistance shall ease the detection of occupancy and identifying the lost object. The comprehended object occupancy idea is utilized to recognize the humans and object detection. The implementation idea shall facilitate the utilization of PIR-based motion detector sensor to recognize human presence as well as SlimYOLOv3 framework to identify objects. Circumstances where the occupancy of humans are counted and object to be identified are the main output.

scholarly journals Design of a Scalable and Fast YOLO for Edge-Computing Devices

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6779
Author(s):  
Byung-Gil Han ◽  
Joon-Goo Lee ◽  
Kil-Taek Lim ◽  
Doo-Hyun Choi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Processing Speed ◽  
Edge Computing ◽  
Light Weight ◽  
Detection Technology ◽  
Accuracy Performance ◽  
Speed And Accuracy

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model.

Hunt for Perfect Detection of Parking Occupancy: Evaluation of On-Street Technology and Its Ability to Address Urban Challenges

2017 ◽  
Vol 2645 (1) ◽  
pp. 12-23 ◽  
Author(s):  
Soumya S. Dey ◽  
Matthew Darst ◽  
Alek Pochowski ◽  
Benito O. Pérez ◽  
Eduardo Cardenas Sanchez
Keyword(s):  
Data Collection ◽  
Real Time ◽  
District Of Columbia ◽  
Battery Life ◽  
Time Data ◽  
Detection Technology ◽  
Occupancy Detection ◽  
Depth Analysis ◽  
Current Usage ◽  
Collection Methods

For better management of on-street parking, cities are searching for ways to understand current usage of their on-street supply. Options for collecting on-street parking occupancy information range from manual data collection, which tends to be slow and not in real time, to in-ground sensors, which can provide real-time data but are costly and may have other issues related to battery life and their placement in the roadbed. This paper evaluates the various data collection methods and technologies for on-street parking. The District of Columbia Department of Transportation’s experience with various on-street technologies to detect parking occupancy is described, with a focus on how the technologies were able to identify solutions to the urban challenges inherent in the District. Criteria for a more in-depth analysis of on-street occupancy detection technology are described and presented as the next steps in the evaluation.

scholarly journals Real-time object detection technology in railway operations

2021 ◽  
pp. 154-162
Author(s):  
Rock K C Ho ◽  
Zhangyu Wang ◽  
Simon S C Tang ◽  
Qiang Zhang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
New Technology ◽  
Practical Application ◽  
Data Set ◽  
Detection Technology ◽  
Railway Industry ◽  
Extreme Scenario ◽  
Typical Scenario

Development of new technology to enhance train operability, in particular during manual driving by real-time object detection on track, is one of the rising trends in the railway industry. The function of object detection can provide train operators with reminder alerts whenever there is an object detected close to a train, e.g. a defined distance from the train. In this paper, a two-stage vision-based method is proposed to achieve this goal. At first, the Targets Generation Stage focuses on extracting all potential targets by identifying the centre points of targets. Meanwhile, the Targets Reconfirmation Stage is further adopted to re-analyse the potential targets from the previous stage to filter out incorrect potential targets in the output. The experiment and evaluation result shows that the proposed method achieved an Average Precision (AP) of 0.876 and 0.526 respectively under typical scenario sub-groups and extreme scenario sub-groups of the data set collected from a real railway environment at the methodological level. Furthermore, at the application level, high performance with the False Alarm Rate (FAR) of 0.01% and Missed Detection Rate (MDR) of 0.94%, which is capable of practical application, was achieved during the operation in the Tsuen Wan Line (TWL) in Hong Kong.

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