18,432 research outputs found
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
High-Accuracy Facial Depth Models derived from 3D Synthetic Data
In this paper, we explore how synthetically generated 3D face models can be
used to construct a high accuracy ground truth for depth. This allows us to
train the Convolutional Neural Networks (CNN) to solve facial depth estimation
problems. These models provide sophisticated controls over image variations
including pose, illumination, facial expressions and camera position. 2D
training samples can be rendered from these models, typically in RGB format,
together with depth information. Using synthetic facial animations, a dynamic
facial expression or facial action data can be rendered for a sequence of image
frames together with ground truth depth and additional metadata such as head
pose, light direction, etc. The synthetic data is used to train a CNN based
facial depth estimation system which is validated on both synthetic and real
images. Potential fields of application include 3D reconstruction, driver
monitoring systems, robotic vision systems, and advanced scene understanding
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