Spatio-temporal prediction of collision candidates for static and dynamic objects in monocular image sequences

This paper presents a novel approach for reactive obstacle avoidance for static and dynamic objects using monocular image sequences. A sparse motion field is calculated by tracking point features using the Kanade-Lucas-Tomasi method. The rotational component of this sparse optical flow due to ego motion of the camera is compensated using motion parameters estimated directly from the images. A robust method for detection of static and dynamic objects in the scene is applied to identify collision candidates. The approach operates entirely in the image space of a monocular camera and does not require any extrinsic information about the configuration of the sensor or speed of the camera. The system prioritizes the detected collision candidates by their time to collision. Additionally, the spatial distribution of the candidates is calculated for non-degenerated conditions. We present the mathematical framework and the experimental validation of the suggested approach on simulated and real-world data.