Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
We have collected large-scale traffic near-miss incident database!
We presents a novel traffic database that contains information on a large number of traffic near-miss incidents. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).
ICRA 2018, CVPR 2018, Sensors Journal (IF:2.78), ICRA 2020
Hirokatsu Kataoka (AIST), Tomoyuki Suzuki, Teppei Suzuki (Keio/AIST), Yoshimitsu Aoki (Keio), Kodai Nakashima, Yutaka Satoh (AIST), Shoko Oikawa, Yasuhiro Matsui (NTSEL)