Vasiliy Karasev, Alper Ayvaci,
Bernd Heisele, Stefano Soatto
University of California LA, Honda Research Institute,
USA.
We present a method to predict long-term motion of pedestrians, modeling their behavior as jump-Markov processes with their goal a hidden variable. Assuming approximately rational behavior, and incorporating environmental constraints and biases, including time-varying ones imposed by traffic lights, we model intent as a policy in a Markov decision process framework. We infer pedestrian state using a Rao-Blackwellized filter, and intent by planning according to a stochastic policy, reflecting individual preferences in aiming at the same goal.
Video.
V. Karasev, A. Ayvaci, B. Heisele, S. Soatto. Intent-Aware Long-Term Prediction of Pedestrian Motion. International Conference on Robotics and Automation (ICRA) , 2016.
Sample long-term predictions of traffic participants’ motion generated by our model. Warmer colors indicate more probable paths. Notice that the predictions are multi-modal and obey constraints of the environment.
Partially Occluded Object Detection by Finding the Visible Features and Parts
Kai Chi Chan, Alper Ayvaci and
Bernd Heisele
Purdue Univ., Honda Research Institute, USA.
We address the partially occluded object detection problem by implementing a model which includes latent visibility flags that are attached to cells and parts of a Deformable Part Model. A visibility flag indicates whether an image portion is part of a pedestrian or part of an occluder. To compute the visibility flags and the score of the detector simultaneously, we maximize a concave objective function that is composed of the following four parts: (1) the detection scores of visible cells and parts, (2) a cell-to-cell consistency term which encourages neighboring cells to have the same visibility flags, (3) a cell-to-part consistency term which encourages compatible labeling among overlapping cells and parts, and (4) a penalty term for cells and parts that are labeled as occluded. The maximization of the concave objective function is done using the Alternating Direction Method of Multipliers (ADMM). By removing scores of occluded cells and parts from the final detection score we significantly improve detection performance on partially occluded pedestrians. In experiments we show that our system outperforms the standard DPM and other state-of-art methods.
K. C. Chan, A. Ayvaci and B. Heisele. Partially Occluded Object Detection by Finding the Visible Features and Parts. International Conference on Image Processing, (ICIP), 2015, Best Paper Award.
Consistency graph: Root cells are represented by the
squares on the image, and parts are drawn above. Edges that are represented by orange lines indicate the cell-to-cell consistency while yellow lines indicate the cell-to-part consistency.
The visibility map estimates: Input image.
The initialization passed to ADMM: To acquire this
map, we threshold the cell-level and part-level
detector responses at 0. Red and green indicate the
variables with values 0 and 1, respectively. The
binarized visibility estimate at first iteration. The
solution at convergence (third iteration).
B. Heisele, G. Kim, and A. Meyer
Honda Research Institute, USA.
We propose techniques for designing and training of pose-invariant object recognition systems using realistic 3d computer graphics models. We look at the relation between the size of the training set and the classification accuracy for a basic recognition task and provide a method for estimating the degree of difficulty of detecting an object. We show how to sample, align, and cluster images of objects on the view sphere. We address the problem of training on large, highly redundant data and propose a novel active learning method which generates compact training sets and compact classifiers.
Top row: 3D computer graphics models used for training and photographes of the real objects used for testing. Middle row: Synthetic images with uniform background. Bottom row: Synthetic images with natural background
B. Heisele,
G. Kim, A. Meyer. Object Recognition with 3D Models.
British Machine Vision Conference, 2009.
