  In this paper, Object Detection and Tracking System (ODTS) in combination
with a well-known deep learning network, Faster Regional Convolution Neural
Network (Faster R-CNN), for Object Detection and Conventional Object Tracking
algorithm will be introduced and applied for automatic detection and monitoring
of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way
Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS
accepts a video frame in time as an input to obtain Bounding Box (BBox) results
by Object Detection and compares the BBoxs of the current and previous video
frames to assign a unique ID number to each moving and detected object. This
system makes it possible to track a moving object in time, which is not usual
to be achieved in conventional object detection frameworks. A deep learning
model in ODTS was trained with a dataset of event images in tunnels to Average
Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car,
Person, and Fire, respectively. Then, based on trained deep learning model, the
ODTS based Tunnel CCTV Accident Detection System was tested using four accident
videos which including each accident. As a result, the system can detect all
accidents within 10 seconds. The more important point is that the detection
capacity of ODTS could be enhanced automatically without any changes in the
program codes as the training dataset becomes rich.
