About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Structural inpainting of road patches for anomaly detection
Abstract
Obstacle detection on the road is a key function for self-driving vehicles. A lot of research has focused on detecting large obstacles such as cars and pedestrians. Small obstacles can also be the source of serious accidents, especially at high speed. We present an approach for detecting anomalies on the road using a higher-order Boltzmann machine. As opposed to conventional anomaly detectors the proposed system learns to inpaint the road patches with commonly occurring road features such as lane markings and expansion dividers, depending on the context. The system does not consider these frequent road artifacts as anomalies and significantly reduces the number of obstacle candidates. We show initial empirical results for anomaly detection with this new approach.