Abstract:
In recent years, vehicle detection for traffic monitoring from urban video surveillance
cameras has become a hot research topic among researchers because of an increase in
anomalous or unusual vehicle activities from video sequences captured from the traffic
surveillance cameras. Instead of manually analyzing the video for detection of anomalies,
there is a need for an automatic process that would easily be easily applied to a large
number of videos, because the number of video surveillance cameras is increasing in the
public places causing the increase in automated analysis of traffic by capturing videos.
Therefore, automatic video surveillance of traffic is considered one of its main
applications. The main purpose of the video-based surveillance system is to analyze
patterns and behavior, vehicle tracking, detection of anomalies, and abnormal event
prediction. In this research work, a novel framework: Vehicle Detection for Traffic
Monitoring from Urban Video Surveillance Camera (VDTMUVSC) using deep neural
networks is proposed to get better results as compared to other state-of-the-art methods
which are being used for automobile detection. In this method, to reduce the time for
training, pre-trained weights are used in terms of transfer learning and some initial layers
from the backbone of architecture are frozen. In the second part, the hyper-parameter
tuning technique is used to achieve higher accuracy. Further, extensive experiments have
been conducted on the benchmark dataset UA-DETRAC which is introduced recently,
especially for the purpose of vehicle detection and tracking. The results demonstrated that
our proposed architecture outperformed existing techniques with a margin of 3% to 5% in
object detection for vehicles, achieving 80.3% mean average precision.
