Neural Network-Based Vehicle and Pedestrian Detection for Video Analysis System

被引：0
|
作者：
Babayan, Pavel V. [1 ]
Ershov, Maksim D. [1 ]
Erokhin, Denis Y. [1 ]
机构：
[1] RSREU, Dept Automat & Informat Technol Control, Ryazan, Russia
来源：
2019 8TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO) | 2019年
关键词：
video analysis; image processing; object detection; pattern recognition; neural networks; machine learning;
D O I：
10.1109/meco.2019.8760125
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In our research we compare various neural network architectures that are used for object detection and recognition. In this work vehicles and pedestrians are considered objects of interest. Modern artificial neural networks are able to detect and localize objects of known classes. This allows them to be used in various technical vision systems and video analysis systems. In this paper we compare three architectures (YOLO, Faster R-CNN, SSD) by the following criteria: processing speed, mAP, precision and recall.
引用
收藏
页码：310 / 314
页数：5
相关论文
共 50 条
  • [41] Intrusion detection system: a deep neural network-based concatenated approach
    Sharma, Hidangmayum Satyajeet
    Singh, Khundrakpam Johnson
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (10): : 13918 - 13948
  • [42] Neural network-based intrusion detection systems
    Hu, LX
    He, ZJ
    COMPUTER SCIENCE AND TECHNOLOGY IN NEW CENTURY, 2001, : 296 - 298
  • [43] An artificial neural network-based fall detection
    Yoo, SunGil
    Oh, Dongik
    INTERNATIONAL JOURNAL OF ENGINEERING BUSINESS MANAGEMENT, 2018, 10
  • [44] HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
    Khan, Muhammad Ashfaq
    PROCESSES, 2021, 9 (05)
  • [45] Vehicle identifying system based on video image processing and fuzzy neural network
    Peng, Xiaohong
    Xie, Shiyi
    Mo, Zhi
    Liao, Riyao
    Journal of Information and Computational Science, 2010, 7 (11): : 2332 - 2339
  • [46] Artificial Neural Network-based Fault Detection
    Khelifi, Asma
    Ben Lakhal, Nadhir Mansour
    Gharsallaoui, Hajer
    Nasri, Othman
    2018 5TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT), 2018, : 1017 - 1022
  • [47] A Convolutional Neural Network-based Approach For Image Analysis and Injection Detection
    Titouna, Chafiq
    Nait-Abdesselam, Farid
    2024 IEEE INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL BASED SURVEILLANCE, AVSS 2024, 2024,
  • [48] Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle
    Ullah, Mati
    Zhao, Chunhui
    Maqsood, Hamid
    Ul Hassan, Mahmood
    Humayun, Muhammad
    INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS, 2023, 11 (02) : 226 - 248
  • [49] Neural Network-based Vehicle Image Classification for IoT Devices
    Payvar, Saman
    Khan, Mir
    Stahl, Rafael
    Mueller-Gritschneder, Daniel
    Boutellier, Jani
    PROCEEDINGS OF THE 2019 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS 2019), 2019, : 148 - 153
  • [50] An Efficient Real-Time Fire Detection Method Using Computer Vision and Neural Network-Based Video Analysis
    Nagulan, S.
    Krishnan, A. N. Srinivasa
    Kumar, A. Kiran
    Kumar, S. Vishnu
    Suchithra, M.
    PROCEEDINGS OF THIRD DOCTORAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE, DOSCI 2022, 2023, 479 : 627 - 637
12345