  Predicting the future trajectory of agents from visual observations is an
important problem for realization of safe and effective navigation of
autonomous systems in dynamic environments. This paper focuses on two important
aspects of future trajectory forecast which are particularly relevant for
mobile platforms: 1) modeling uncertainty of the predictions, particularly from
egocentric views, where uncertainty in the interactive reactions and behaviors
of other agents must consider the uncertainty in the ego-motion, and 2)
modeling multi-modality nature of the problem, which are particularly prevalent
at junctions in urban traffic scenes. To address these problems in a unified
approach, we propose NEMO (Noisy Ego MOtion priors for future object
localization) for future forecast of agents in the egocentric view. In the
proposed approach, a predictive distribution of future forecast is jointly
modeled with the uncertainty of predictions. For this, we divide the problem
into two tasks: future ego-motion prediction and future object localization. We
first model the multi-modal distribution of future ego-motion with uncertainty
estimates. The resulting distribution of ego-behavior is used to sample
multiple modes of future ego-motion. Then, each modality is used as a prior to
understand the interactions between the ego-vehicle and target agent. We
predict the multi-modal future locations of the target from individual modes of
the ego-vehicle while modeling the uncertainty of the target's behavior. To
this end, we extensively evaluate the proposed framework using the publicly
available benchmark dataset (HEV-I) supplemented with odometry data from an
Inertial Measurement Unit (IMU).
