19,549 research outputs found
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.Comment: To appear in CVPR 201
ARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologies
The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624
ARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologies
The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
Joint Detection and Tracking in Videos with Identification Features
Recent works have shown that combining object detection and tracking tasks,
in the case of video data, results in higher performance for both tasks, but
they require a high frame-rate as a strict requirement for performance. This is
assumption is often violated in real-world applications, when models run on
embedded devices, often at only a few frames per second.
Videos at low frame-rate suffer from large object displacements. Here
re-identification features may support to match large-displaced object
detections, but current joint detection and re-identification formulations
degrade the detector performance, as these two are contrasting tasks. In the
real-world application having separate detector and re-id models is often not
feasible, as both the memory and runtime effectively double.
Towards robust long-term tracking applicable to reduced-computational-power
devices, we propose the first joint optimization of detection, tracking and
re-identification features for videos. Notably, our joint optimization
maintains the detector performance, a typical multi-task challenge. At
inference time, we leverage detections for tracking (tracking-by-detection)
when the objects are visible, detectable and slowly moving in the image. We
leverage instead re-identification features to match objects which disappeared
(e.g. due to occlusion) for several frames or were not tracked due to fast
motion (or low-frame-rate videos). Our proposed method reaches the
state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge
among online trackers, and 3rd overall.Comment: Accepted at Image and Vision Computing Journa
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