28,988 research outputs found
Visual feature tracking based on PHD filter for vehicle detection
Vehicle detection is one of the classical application
among the Advance Driver Assistance Systems (ADAS).
Applications like emergency braking or adaptive cruise control
(ACC) require accurate and reliable vehicle detection. In latest
years the improvements in vision detection have lead to the
introduction of computer vision to detect vehicles by means of
these more economical sensors, with high reliability.
In the present paper, a novel algorithm for vehicle detection
and tracking based on a probability hypothesis density (PHD)
filter is presented. The first detection is based on a fast machine
learning algorithm (Adaboost) and Haar-Like features. Later,
the tracking is performed, by means features detected within the
bounding box provided by the vehicle detection. The features, are
tracked by a PHD filter. The results of the features being tracked
are combined together in the last step, based on several different
methods. Test provided show the performance of the PHD filter
in public sequences using the different methods proposed.This work was supported by the Spanish Government
through the Cicyt projects (GRANT TRA2010-20225-C03-01)
and (GRANT TRA 2011-29454-C03-02)
PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability
This paper considers the object detection and tracking problem in a road traffic situation from a traffic participantâs perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and its Statistical Meaning
This paper presents a model-based method for fusing data from multiple
sensors with a hypothesis-test-based component for rejecting potentially faulty
or otherwise malign data. Our framework is based on an extension of the classic
particle filter algorithm for real-time state estimation of uncertain systems
with nonlinear dynamics with partial and noisy observations. This extension,
based on classical statistical theories, utilizes statistical tests against the
system's observation model. We discuss the application of the two major
statistical testing frameworks, Fisherian significance testing and
Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion
settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has
a reliable model of faulty data, while the Fisher one is applicable when one
may not have a model of faults, which may occur when dealing with third-party
data, like GNSS data of transportation system users. These statistical tests
can be combined with a particle filter to obtain a Monte Carlo state estimation
scheme that is robust to faulty or outlier data. We present a synthetic freeway
traffic state estimation problem where the filters are able to reject simulated
faulty GNSS measurements. The fault-model-free Fisher filter, while
underperforming the Neyman-Pearson one when the latter has an accurate fault
model, outperforms it when the assumed fault model is incorrect.Comment: IEEE Intelligent Transportation Systems Magazine, special issue on
GNSS-based positionin
Video analysis based vehicle detection and tracking using an MCMC sampling framework
This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Detection of variance changes and mean value jumps in measurement noise for multipath mitigation in urban navigation
This paper studies an urban navigation filter for land vehicles. Typical urban-canyon phenomena as multipath and GPS outages seriously degrade positioning performance. To deal with these scenarios a hybrid navigation system using GPS and dead-reckoning sensors is presented. This navigation system is complemented by a two-step detection procedure that classifies outliers according to their associated source of error. Two different situations will be considered in the presence of multipath. These situations correspond to the presence or absence of line of sight for the different GPS satellites. Therefore, two kinds of errors are potentially âcorruptingâ the pseudo-ranges, modeled as variance changes or mean value jumps in noise measurements. An original multiple model approach is proposed to detect, identify and correct these errors and provide a final consistent solution
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