Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Understanding human motion behavior is critical for autonomous moving platforms (like self-
driving cars and social robots) if they are to navigate human-centric environments. This is …

This paper addresses the problem of path prediction for multiple interacting agents in a
scene, which is a crucial step for many autonomous platforms such as self-driving cars and …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Novelty detection is the process of identifying the observation (s) that differ in some respect
from the training observations (the target class). In reality, the novelty class is often absent …

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …

Humans navigate complex crowded environments based on social conventions: they
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …

Reliable prediction of surround vehicle motion is a critical requirement for path planning for
autonomous vehicles. In this paper, we propose a unified framework for surround vehicle …