Bouraffa, Tayssir (Chalmers University of Technology), Kjellberg Carlson, Elias (Chalmers University of Technology), Wessman, Erik (University), nouri, ali (Chalmers), Lamart, Pierre (University of Gothenburg), Berger, Christian (Chalmers | University of Gothenburg)

Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space Filling Curves

Scheduled for presentation during the Regular Session "Traffic prediction and estimation I" (WeAT6), Wednesday, September 25, 2024, 10:50−11:10, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Gathering data and identifying events in various traffic situations remain an essential challenge for the systematic evaluation of a perception system’s performance. Analyzing large-scale, typically unstructured, multi- modal, time-series data obtained from video, radar, and LiDAR is computationally demanding, particularly when meta-information or annotations are missing. We compare Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient event detection via space-filling curves on video data from a forward-facing, in-vehicle camera. Our first approach leverages unexpected disturbances in the OF field from vehicle surroundings; the second approach is a DL model trained on human visual attention to predict a driver’s gaze to spot potential event locations. We feed these results to a space-filling curve to reduce dimensionality and achieve computationally efficient event retrieval. We systematically evaluate our concept by obtaining characteristic patterns for both approaches from a large-scale virtual dataset (SMIRK) and applied our findings to the Zenseact Open Dataset (ZOD), a large multi-modal, real-world dataset, collected over two years in 14 different European countries. Our results yield that the OF approach excels in specificity and reduces false positives, while the DL approach demonstrates superior sensitivity. Both approaches offer comparable processing speed, making them suitable for real-time applications.