Abstract: Modern advanced driver assistance systems (ADAS) rely on a range of sensors including radar, ultrasound, LIDAR and cameras. Active sensors have found applications in detecting traffic participants (TPs) such as cars or pedestrians and scene elements (SEs) such as roads. However, camera-based systems have the potential to achieve or augment these capabilities at a much lower cost, while allowing new ones such as determination of TP and SE semantics as well as their interactions in complex traffic scenes.

In this talk, we present several technical advances for vision-based ADAS. A common theme is to overcome the challenges posed by lack of large-scale annotations in deep learning frameworks. We introduce approaches to correspondence estimation that are trained on purely synthetic data but adapt well to real data at test-time. We introduce object detectors that are light enough for ADAS, trained with knowledge distillation to retain accuracies of deeper architectures. Our semantic segmentation methods are trained on weak supervision that requires only a tenth of conventional annotation time. We propose methods for 3D reconstruction that use deep supervision to recover fine TP part locations while relying on purely synthetic 3D CAD models. We develop deep learning frameworks for multi-target tracking, as well as occlusion-reasoning in TP localization and SE layout estimation. Finally, we present a framework for TP behavior prediction in complex traffic scenes that accounts for TP-TP and TP-SE interactions. Our approach allows prediction of diverse multimodal outcomes and aims to account for long-term strategic behaviors in complex scenes.

Bio: Manmohan Chandraker is an assistant professor at the CSE department of the University of California, San Diego and leads the computer vision research effort at NEC Labs America in Cupertino. He received a B.Tech. in Electrical Engineering at the Indian Institute of Technology, Bombay and a PhD in Computer Science at the University of California, San Diego. His personal research interests are 3D scene understanding and reconstruction, with applications to autonomous driving and human-computer interfaces. His works have received the Marr Prize Honorable Mention for Best Paper at ICCV 2007, the 2009 CSE Dissertation Award for Best Thesis at UCSD, a PAMI special issue on best papers of CVPR 2011 and the Best Paper Award at CVPR 2014.