138,372 research outputs found
Deep Learning-Based Tracking of Multiple Objects in the Context of Farm Animal Ethology
Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging task to automatically recognise and track individual animals. Those difficulties can be, for example, the similarity of animals of the same species which makes distinguishing between them harder, or sudden changes in their body shape which may happen due to putting on or spreading out the wings in a very short period of time. In this paper, an automatic poultry tracking algorithm is proposed. This algorithm is based on the well-known tracktor approach and tackles multi-object tracking by exploiting the regression head of the Faster R-CNN model to perform temporal realignment of object bounding boxes. Additionally, we use a multi-scale re-identification model to improve the re-association of the detected animals. For evaluating the performance of the proposed method in this study, a novel dataset consisting of seven image sequences that show chicks in an average pen farm in different stages of growth is used
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
Multi-point tracking is a challenging task that involves detecting points in
the scene and tracking them across a sequence of frames. Computing
detection-based measures like the F-measure on a frame-by-frame basis is not
sufficient to assess the overall performance, as it does not interpret
performance in the temporal domain. The main evaluation metric available comes
from Multi-object tracking (MOT) methods to benchmark performance on datasets
such as KITTI with the recently proposed higher order tracking accuracy (HOTA)
metric, which is capable of providing a better description of the performance
over metrics such as MOTA, DetA, and IDF1. While the HOTA metric takes into
account temporal associations, it does not provide a tailored means to analyse
the spatial associations of a dataset in a multi-camera setup. Moreover, there
are differences in evaluating the detection task for points when compared to
objects (point distances vs. bounding box overlap). Therefore in this work, we
propose a multi-view higher order tracking metric (mvHOTA) to determine the
accuracy of multi-point (multi-instance and multi-class) tracking methods,
while taking into account temporal and spatial associations.mvHOTA can be
interpreted as the geometric mean of detection, temporal, and spatial
associations, thereby providing equal weighting to each of the factors. We
demonstrate the use of this metric to evaluate the tracking performance on an
endoscopic point detection dataset from a previously organised surgical data
science challenge. Furthermore, we compare with other adjusted MOT metrics for
this use-case, discuss the properties of mvHOTA, and show how the proposed
multi-view Association and the Occlusion index (OI) facilitate analysis of
methods with respect to handling of occlusions. The code is available at
https://github.com/Cardio-AI/mvhota.Comment: 16 pages, 9 figure
Real-Time Bird's Eye View Multi-Object Tracking system based on Fast Encoders for object detection
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), September 20-23, 2020, Rhodes, Greece. Virtual Conference.This paper presents a Real-Time Bird’s Eye View
Multi Object Tracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for object
detection and a combination of Hungarian algorithm and
Bird’s Eye View (BEV) Kalman Filter, respectively used for
data association and state estimation. The system is able to
analyze 360 degrees around the ego-vehicle as well as estimate
the future trajectories of the environment objects, being the
essential input for other layers of a self-driving architecture,
such as the control or decision-making. First, our system
pipeline is described, merging the concepts of online and realtime DATMO (Deteccion and Tracking of Multiple Objects),
ROS (Robot Operating System) and Docker to enhance the
integration of the proposed MOT system in fully-autonomous
driving architectures. Second, the system pipeline is validated
using the recently proposed KITTI-3DMOT evaluation tool that
demonstrates the full strength of 3D localization and tracking
of a MOT system. Finally, a comparison of our proposal with
other state-of-the-art approaches is carried out in terms of
performance by using the mainstream metrics used on MOT
benchmarks and the recently proposed integral MOT metrics,
evaluating the performance of the tracking system over all
detection thresholds.Ministerio de Ciencia, InnovaciĂłn y UniversidadesComunidad de Madri
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks. 1
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In recent years, numerous effective multi-object tracking (MOT) methods are
developed because of the wide range of applications. Existing performance
evaluations of MOT methods usually separate the object tracking step from the
object detection step by using the same fixed object detection results for
comparisons. In this work, we perform a comprehensive quantitative study on the
effects of object detection accuracy to the overall MOT performance, using the
new large-scale University at Albany DETection and tRACking (UA-DETRAC)
benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging
video sequences captured from real-world traffic scenes (over 140,000 frames
with rich annotations, including occlusion, weather, vehicle category,
truncation, and vehicle bounding boxes) for object detection, object tracking
and MOT system. We evaluate complete MOT systems constructed from combinations
of state-of-the-art object detection and object tracking methods. Our analysis
shows the complex effects of object detection accuracy on MOT system
performance. Based on these observations, we propose new evaluation tools and
metrics for MOT systems that consider both object detection and object tracking
for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI
Planar Object Tracking in the Wild: A Benchmark
Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201
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