127,918 research outputs found
3D Tracking Using Multi-view Based Particle Filters
Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios
Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model
Three-dimensional tracking of multiple objects from multiple views has a wide
range of applications, especially in the study of bio-cluster behavior which
requires precise trajectories of research objects. However, there are
significant temporal-spatial association uncertainties when the objects are
similar to each other, frequently maneuver, and cluster in large numbers.
Aiming at such a multi-view multi-object 3D tracking scenario, a current
statistical model based Kalman particle filter (CSKPF) method is proposed
following the Bayesian tracking-while-reconstruction framework. The CSKPF
algorithm predicts the objects' states and estimates the objects' state
covariance by the current statistical model to importance particle sampling
efficiency, and suppresses the measurement noise by the Kalman filter. The
simulation experiments prove that the CSKPF method can improve the tracking
integrity, continuity, and precision compared with the existing constant
velocity based particle filter (CVPF) method. The real experiment on fruitfly
clusters also confirms the effectiveness of the CSKPF method.Comment: 12 pages, 12 figure
Integration of the 3D Environment for UAV Onboard Visual Object Tracking
Single visual object tracking from an unmanned aerial vehicle (UAV) poses
fundamental challenges such as object occlusion, small-scale objects,
background clutter, and abrupt camera motion. To tackle these difficulties, we
propose to integrate the 3D structure of the observed scene into a
detection-by-tracking algorithm. We introduce a pipeline that combines a
model-free visual object tracker, a sparse 3D reconstruction, and a state
estimator. The 3D reconstruction of the scene is computed with an image-based
Structure-from-Motion (SfM) component that enables us to leverage a state
estimator in the corresponding 3D scene during tracking. By representing the
position of the target in 3D space rather than in image space, we stabilize the
tracking during ego-motion and improve the handling of occlusions, background
clutter, and small-scale objects. We evaluated our approach on prototypical
image sequences, captured from a UAV with low-altitude oblique views. For this
purpose, we adapted an existing dataset for visual object tracking and
reconstructed the observed scene in 3D. The experimental results demonstrate
that the proposed approach outperforms methods using plain visual cues as well
as approaches leveraging image-space-based state estimations. We believe that
our approach can be beneficial for traffic monitoring, video surveillance, and
navigation.Comment: Accepted in MDPI Journal of Applied Science
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection
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