8,060 research outputs found
Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs
Mobility On Demand (MOD) systems are revolutionizing transportation in urban
settings by improving vehicle utilization and reducing parking congestion. A
key factor in the success of an MOD system is the ability to measure and
respond to real-time customer arrival data. Real time traffic arrival rate data
is traditionally difficult to obtain due to the need to install fixed sensors
throughout the MOD network. This paper presents a framework for measuring
pedestrian traffic arrival rates using sensors onboard the vehicles that make
up the MOD fleet. A novel distributed fusion algorithm is presented which
combines onboard LIDAR and camera sensor measurements to detect trajectories of
pedestrians with a 90% detection hit rate with 1.5 false positives per minute.
A novel moving observer method is introduced to estimate pedestrian arrival
rates from pedestrian trajectories collected from mobile sensors. The moving
observer method is evaluated in both simulation and hardware and is shown to
achieve arrival rate estimates comparable to those that would be obtained with
multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS).
http://ieeexplore.ieee.org/abstract/document/7759357
Robust pedestrian detection and tracking in crowded scenes
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases
Real-Time RGB-D based Template Matching Pedestrian Detection
Pedestrian detection is one of the most popular topics in computer vision and
robotics. Considering challenging issues in multiple pedestrian detection, we
present a real-time depth-based template matching people detector. In this
paper, we propose different approaches for training the depth-based template.
We train multiple templates for handling issues due to various upper-body
orientations of the pedestrians and different levels of detail in depth-map of
the pedestrians with various distances from the camera. And, we take into
account the degree of reliability for different regions of sliding window by
proposing the weighted template approach. Furthermore, we combine the
depth-detector with an appearance based detector as a verifier to take
advantage of the appearance cues for dealing with the limitations of depth
data. We evaluate our method on the challenging ETH dataset sequence. We show
that our method outperforms the state-of-the-art approaches.Comment: published in ICRA 201
Pedestrian detection in uncontrolled environments using stereo and biometric information
A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios
Survey on Vision-based Path Prediction
Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.Comment: DAPI 201
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