Anomaly Detection in Traffic Trajectories Using a Combination of Fuzzy, Deep Convolutional and Autoencoder Networks

  • سال انتشار: 1400
  • محل انتشار: مجله مهندسی کامپیوتر و دانش، دوره: 4، شماره: 2
  • کد COI اختصاصی: JR_CKE-4-2_001
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 212
دانلود فایل این مقاله

نویسندگان

Mojtaba Banifakhr

Azadi Campus, Yazd University, Yazd, Iran, Email:Banifakhr.

Mohammad Taghi Sadeghi

Department of Electrical Engineering, Yazd University, Yazd, Iran.

چکیده

Due to the increasing deployment of vehicles in human societies and the necessity for smart traffic control, anomaly detection is among the various tasks widely employed in traffic monitoring. As the issue of urban traffic and their relative smart monitoring systems have gained popularity among researchers in recent years, there exist several studies in this regard. In most of these studies, classification is performed based on the behavior of drivers, where a set of default trajectories are used in order to learn the system and classify the related data. However, two under-studied challenges are the lack of access to sufficient data to provide an efficient model, along with the lack of access to anomaly data that covers all possible abnormal trajectories. While the former challenge can be tackled through long-term data recording, the latter requires appropriate considerations. To this aim, we have utilized a combination of optimized convolutional neural network and fuzzy neural network classifiers, along with autoencoding neural networks. The final combination occurs at the decision level. First, the CNN-ANFIS classifier assigns the input trajectory to one of the predefined categories. Then, the trained autoencoder networks examine the result in order to find whether the trajectory is normal or abnormal. Obtaining ۸۷.۵% accuracy on QMUL and ۹۹.۵% on the T۱۵ datasets confirms the superior performance of the proposed method. 

کلیدواژه ها

Neural-Fuzzy Inference System, Autoencoder Neural Network, Deep Neural Network, Trajectory Anomaly Detection

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.