it for several seconds in advance. For this purpose, we propose in this study a long-term prediction approach based on a combined trajectory classification and particle filter framework. As a measure for the similarity between trajectories, we introduce the quaternion-based rotationally invariant longest common

-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance. Index Terms

Long-term memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay permitting the use of more than one reference frame for motion compensation. This extension provides improved rate-distortion performance. However, motion compensation

states of a vehicle only provide plausible prediction for a short term time horizon, we show that our approach achieves a reasonable motion hypothesis for long prediction intervals especially in complex scenarios as road intersections.

The goal of this effort is to develop methods for imaging riverine flows that can be applied under operational Naval scenarios to aide in the prediction of relevant riverine conditions (e.g. hydraulic depth, thalweg position, etc.) and that are consistent with related efforts to develop analytic

Motion-compensating long-term memory prediction extends the spatial displacement utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion compensation. The long-term memory covers the decoded frames of some

Abstract—Long-term memory motion-compensated prediction extends the spatial displacement vector utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion-compensated prediction. The long-term memory covers

Motion-compensating long-term memory predict.ion ex tends the spatial disJ?lacement utilized in block-based hybrid video coding bl) ' a variable time delay permitting the use of more frames than the previously decoded one for motion compensa!tion. The long-term memory co vers the decoded

Long-term memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay permitting the use of more than one reference frame for motion compensation. This extension leads to improved rate-distortion performance. However, motion compensation

-dimension structure of the scene, without prior assumptions about the camera’s motion or an existing map of the scene. However, existing shape-from-motion algorithms do not address the problem of accumulated long-term drift in the estimated motion and scene structure, which is critical in autonomous vehicle