4 accepted papers
Real-time, accurate prediction of human steering behaviors has wide applications, from developing intelligent traffic systems to deploying autonomous driving systems in both real and simulated worlds. In this letter, we present ContextVAE, a context-aware approach for multi-modal vehicle trajectory
"Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision makin…
We propose a novel real-time algorithm for estimating the local scale correction of a monocular SLAM system, to obtain a correctly scaled version of the 3D map and of the camera trajectory. Within a Bayesian framework, it integrates observations from a deep-learning based generic object detector and…
We propose a methodology for learning and using a multiple-goal probabilistic motion model within a particle filter-based target tracking on video streams. In a set of training video sequences, we first extract the locations (coined as “goals”) where the pedestrians either leave the scene or often c…