عنوان انگلیسی مقاله:
An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety
ترجمه فارسی عنوان مقاله:
یک تشخیص ناهنجاری مبتنی بر یادگیری عمیق در معابر پیاده برای ایمنی کاربران جاده ای آسیب پذیر
Sciencedirect - Elsevier - Safety Science, 142 (2021) 105356: doi:10:1016/j:ssci:2021:105356
Irina V. Pustokhina
Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. Therefore, this paper develops an automated deep learning based anomaly detectiontechnique in pedestrian walkways (DLADT-PW) for vulnerable road user’s safety. The goal of the DLADT-PWmodel is to detect and classify the various anomalies that exist in the pedestrian walkways such as cars, skating, jeep, etc. The DLADT-PW model involves preprocessing as the primary step, which is applied for removing the noise and raise the quality of the image. In addition, mask region convolutional neural network (Mask-RCNN) with densely connected networks (DenseNet) model is employed for the detection process. To ensure the better anomaly detection performance of the DLADT-PW technique, an extensive set of simulations were performed and the outcomes are investigated under distinct aspects. The obtained experimental values confirmed the superior characteristics of the DLADT-PW technique by achieving a maximum detection accuracy.
Keywords: Anomaly detection | Pedestrian walkways | Deep learning | Safety | Mask RCNN