Motrcycling-Net: A Segmentation Approach for Detecting Motorcycling near Misses

Abstract

Commercial motorcycling is one of the economical means of transportation in many countries, although many perceive it as a dangerous means of transportation, which is affirmed by the number of casualties recorded daily. This life threatening record has greatly hindered continuous support for commercial motorcycling as an affordable means of transportation. Information retrieved from near miss datasets can be a telltale of potential hazards and their prevention. However, many researchers have come up with different definitions for near misses, and this has created a gap in applying the right method to near misses, thereby making it statistically difficult to address the situation for a safer commercial motorcycling. In this paper, we present near misses as corrective and preventive measures to safety events. Our focus is on the risk factors of commercial motorcycling near miss incidents, which we address by proposing a near miss detection framework based on deep learning and its models. Video streams of near miss datasets containing motorcycling in different scenes were collected for the experiment. We employed YOLOv4-DeepSort model for the detection and tracking tasks, and the tracked images and identity information were stored. Every 1s, the sequence of image was fetched into the VGG16-BiLSTM model (VGG16 and BiLSTM were used for extraction of image feature information and near misses recognition respectively). We evaluate the method by testing 444 sequential video frames of motorcycling near miss incidents in urban environment, and approximately 96% recognition accuracy rate is achieved. The results of the study indicate practicality for automatic detection of motorcycling near misses in urban environment, and it could assist in providing resourceful technical reference for analyzing the risk factors of motorcycling near misses.