Temporal difference based adaptive object Detection (ToDo) platform at Edge Computing System

Yang, Yousung; Han, Kug; Lee, Seongsoo; Lee, Joohyung

doi:10.1109/ccnc46108.2020.9045513

Cited by 8 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) MEC-empowered network slicing: As discussed in Section IV-A, smart transportation applications such as autonomous driving and infotainment services often have high computational requirements on computing facilities. For example, autonomous driving often needs to deal with computer vision tasks like object detection, which nevertheless requires extensively training deep learning models using massive data [111]. Meanwhile, the training process of these DL models is often required to be done at remote clouds equipped with Graphics Processing Units (GPUs).…”

Section: B Network Slice Orchestration and Managementmentioning

confidence: 99%

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Dai²,

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

166

View full text Add to dashboard Cite

show abstract

Section: B Network Slice Orchestration and Managementmentioning

confidence: 99%

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Dai²,

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

166

View full text Add to dashboard Cite

show abstract

“…The location of the device that captures the images from a position that alludes to an aerial view, generates a reduction in the errors in the detections and allows a greater number of people to intervene during the processing of the images. Yang et al [21] developed a system for adaptive object detection based on time differences. The detection is done through a platform called ToDo, focused on the tracking and tracing of objects, previously trained with the YOLO algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Background subtraction and yolo algorithm

Rondón¹,

Casadiego²,

Delgado³

2021

ing. Solidar

View full text Add to dashboard Cite

Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.

show abstract

TempDiff: Temporal Difference-Based Feature Map-Level Sparsity Induction in CNNs with <4% Memory Overhead

Alwis

Alioto

2021

2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

View full text Add to dashboard Cite

Temporal difference based adaptive object Detection (ToDo) platform at Edge Computing System

Cited by 8 publications

References 3 publications

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Background subtraction and yolo algorithm

TempDiff: Temporal Difference-Based Feature Map-Level Sparsity Induction in CNNs with <4% Memory Overhead

Contact Info

Product

Resources

About