50 research outputs found
Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches
Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide
Sparse Overcomplete Word Vector Representations
Current distributed representations of words show little resemblance to
theories of lexical semantics. The former are dense and uninterpretable, the
latter largely based on familiar, discrete classes (e.g., supersenses) and
relations (e.g., synonymy and hypernymy). We propose methods that transform
word vectors into sparse (and optionally binary) vectors. The resulting
representations are more similar to the interpretable features typically used
in NLP, though they are discovered automatically from raw corpora. Because the
vectors are highly sparse, they are computationally easy to work with. Most
importantly, we find that they outperform the original vectors on benchmark
tasks.Comment: Proceedings of ACL 201
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