Abstract:
Since there has been an increase in the number of cars on the road, there is need to fast identify
accident-prone areas and respond to them promptly. This Thesis aims at finding the suitability
of employing state-of-the-art deep learning algorithms in identifying traffic accidents and their
severity through video surveillance systems. To achieve this, using the large set of traffic
videos, we learned several deep learning models which could identify whether an accident
occurred based on the video or show how severe the accident was. The main model used in this
research, called the Long-term Recurrent Convolutional Network (LRCN), combines two
powerful techniques: The first is to distinguish between the fine details of the video at a frame
level while the second is used in capturing the sequence of events in the material. Apart from
the LRCN model, several other models like LSTM, 3D CNN, and MoVinet were also used for
evaluation for better understanding of the proposed method’s performance and its variations
with other models. The proposed LRCN model was unique by presenting a high degree of
accurate identification of incidents and their severity. Processing of the videos gave it a unique
chance of capturing both the spatial and temporal aspects, hence seeing patterns, which other
models failed on. These findings underscore the ability of LRCN in revolutionizing how we
monitor traffic accidents hence making the roads safer and with better response time to the
incidents. It provides the way out to realize more sophisticated traffic management systems,
which can recognize the cases of the accidents, the urgency of responses, and may subsequently
reduce the number of fatalities related to the traffic incidents.
