Book Chapter10.1007/978-3-642-33765-9_15
Activity forecasting
Kris M. Kitani,Brian D. Ziebart,James Andrew Bagnell,Martial Hebert +3 more
- 07 Oct 2012
pp 201-214
879
TL;DR: In this article, the authors address the task of inferring the future actions of people from noisy visual input by using state-of-the-art semantic scene understanding combined with ideas from optimal control theory.
read more
Abstract: We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran,Du Tran,Lubomir Bourdev,Rob Fergus,Lorenzo Torresani,Manohar Paluri +5 more
- 07 Dec 2015
TL;DR: The learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks.
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alexandre Alahi,Kratarth Goel,Vignesh Ramanathan,Alexandre Robicquet,Li Fei-Fei,Silvio Savarese +5 more
- 27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
•Posted Content
Learning Spatiotemporal Features with 3D Convolutional Networks
TL;DR: In this article, the authors proposed a simple and effective approach for spatio-temporal feature learning using deep 3D convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
3.8K
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Agrim Gupta,Justin Johnson,Li Fei-Fei,Silvio Savarese,Alexandre Alahi +4 more
- 29 Mar 2018
TL;DR: A recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.
•Posted Content
Generating Videos with Scene Dynamics
TL;DR: The authors proposed a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background, which can generate tiny videos up to a second at full frame rate better than simple baselines.
1.4K
References
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
•Book
Neural Networks: A Comprehensive Foundation
Simon Haykin
- 16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Regularization Paths for Generalized Linear Models via Coordinate Descent
TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.