Modeling and prediction of human behavior
Neural Computation
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Constraints on Deformable Models with Applications to Face Tracking
International Journal of Computer Vision
Robust Real-Time Detection, Tracking, and Pose Estimation of Faces in Video Streams
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance
IEEE Pervasive Computing
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis
Computer Vision and Image Understanding
A two-stage head pose estimation framework and evaluation
Pattern Recognition
A novel active heads-up display for driver assistance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Physics-Based Person Tracking Using the Anthropomorphic Walker
International Journal of Computer Vision
Mechanical and perceptual analyses of human foot movements in pedal operation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
IEEE Transactions on Intelligent Transportation Systems
Vision-based infotainment user determination by hand recognition for driver assistance
IEEE Transactions on Intelligent Transportation Systems
View-invariant gesture recognition using 3D optical flow and harmonic motion context
Computer Vision and Image Understanding
Active deceleration support in car following
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-Road Prediction of Driver's Intent with Multimodal Sensory Cues
IEEE Pervasive Computing
Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety
IEEE Transactions on Intelligent Transportation Systems
Enhanced human-machine interface in braking
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Driver-vehicle confluence or how to control your car in future?
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier
Engineering Applications of Artificial Intelligence
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Understanding driver behavior is an essential component in human-centric Intelligent Driver Assistance Systems. Specifically, driver foot behavior is an important factor in controlling the vehicle, though there have been very few research studies on analyzing foot behavior. While embedded pedal sensors may reveal some information about driver foot behavior, using vision-based foot behavior analysis has additional advantages. The foot movement before and after a pedal press can provide valuable information for better semantic understanding of driver behaviors, states, and styles. They can also be used to gain a time advantage in predicting a pedal press before it actually happens, which is very important for providing proper assistance to driver in time critical (e.g. safety related) situations. In this paper, we propose and develop a new vision based framework for driver foot behavior analysis using optical flow based foot tracking and a Hidden Markov Model (HMM) based technique to characterize the temporal foot behavior. In our experiment with a real-world driving testbed, we also use our trained HMM foot behavior model for prediction of brake and acceleration pedal presses. The experimental results over different subjects provided high accuracy (~94% on average) for both foot behavior state inference and pedal press prediction. By 133ms before the actual press, ~74% of the pedal presses were predicted correctly. This shows the promise of applying this approach for real-world driver assistance systems.