1,480 research outputs found
Learning Spatial Distribution of Long-Term Trackers Scores
Long-Term tracking is a hot topic in Computer Vision. In this context,
competitive models are presented every year, showing a constant growth rate in
performances, mainly measured in standardized protocols as Visual Object
Tracking (VOT) and Object Tracking Benchmark (OTB). Fusion-trackers strategy
has been applied over last few years for overcoming the known re-detection
problem, turning out to be an important breakthrough. Following this approach,
this work aims to generalize the fusion concept to an arbitrary number of
trackers used as baseline trackers in the pipeline, leveraging a learning phase
to better understand how outcomes correlate with each other, even when no
target is present. A model and data independence conjecture will be evidenced
in the manuscript, yielding a recall of 0.738 on LTB-50 dataset when learning
from VOT-LT2022, and 0.619 by reversing the two datasets. In both cases,
results are strongly competitive with state-of-the-art and recall turns out to
be the first on the podium.Comment: 20 pages, 11 figures, 3 table
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes
Monocular depth reconstruction of complex and dynamic scenes is a highly
challenging problem. While for rigid scenes learning-based methods have been
offering promising results even in unsupervised cases, there exists little to
no literature addressing the same for dynamic and deformable scenes. In this
work, we present an unsupervised monocular framework for dense depth estimation
of dynamic scenes, which jointly reconstructs rigid and non-rigid parts without
explicitly modelling the camera motion. Using dense correspondences, we derive
a training objective that aims to opportunistically preserve pairwise distances
between reconstructed 3D points. In this process, the dense depth map is
learned implicitly using the as-rigid-as-possible hypothesis. Our method
provides promising results, demonstrating its capability of reconstructing 3D
from challenging videos of non-rigid scenes. Furthermore, the proposed method
also provides unsupervised motion segmentation results as an auxiliary output
The eighth visual object tracking VOT2020 challenge results
The Visual Object Tracking challenge VOT2020 is the eighth
annual tracker benchmarking activity organized by the VOT initiative.
Results of 58 trackers are presented; many are state-of-the-art trackers
published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges
focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance
and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term
tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only
the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and
introduction of segmentation ground truth in the VOT-ST2020 challenge
– bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds
standard baselines. The source code for most of the trackers is publicly
available from the VOT page. The dataset, the evaluation kit and the
results are publicly available at the challenge website
Binary matrix for pedestrian tracking in infrared images
The primary goal of this thesis is to present a robust low compute cost pedestrian tracking system for use with thermal infra-red images. Pedestrian tracking employs two distinct image analysis tasks, pedestrian detection and path tracking. This thesis will focus on benchmarking existing pedestrian tracking systems and using this to evaluate the proposed pedestrian detection and path tracking algorithm.The first part of the thesis describes the imaging system and the image dataset collected for evaluating pedestrian detection and tracking algorithms. The texture content of the images from the imaging system are evaluated using fourier maps following this the locations at which the dataset was collected are described.The second part of the thesis focuses on the detection and tracking system. To evaluate the performance of the tracking system, a time per target metric is described and is shown to work with existing tracking systems. A new pedestrian aspect ratio based pedestrian detection algorithm is proposed based on a binary matrix dynamically constrained using potential target edges. Results show that the proposed algorithm is effective at detecting pedestrians in infrared images while being less resource intensive as existing algorithms.The tracking system proposed uses deformable, dynamically updated codebook templates to track pedestrians in an infrared image sequence. Results show that this tracker performs as well as existing tracking systems in terms of accuracy, but requires fewer resources
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