Spatio-Temporal Context Modeling for Road Obstacle Detection

TL;DR

This paper presents a novel approach for road obstacle detection that combines spatial scene context modeling with temporal information from image sequences to enhance detection robustness and accuracy.

Contribution

The paper introduces a spatio-temporal context modeling framework that integrates scene layout with object detection and optical flow tracking for improved obstacle detection.

Findings

Outperforms existing methods on SOD and Lost and Found datasets.

Enhances detection robustness by combining spatial context with temporal tracking.

Demonstrates significant improvements in detection accuracy and reliability.

Abstract

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection.