2,678 research outputs found
Novel data association methods for online multiple human tracking
PhD ThesisVideo-based multiple human tracking has played a crucial role in many applications
such as intelligent video surveillance, human behavior analysis, and
health-care systems. The detection based tracking framework has become
the dominant paradigm in this research eld, and the major task is to accurately
perform the data association between detections across the frames.
However, online multiple human tracking, which merely relies on the detections
given up to the present time for the data association, becomes more
challenging with noisy detections, missed detections, and occlusions. To
address these challenging problems, there are three novel data association
methods for online multiple human tracking are presented in this thesis,
which are online group-structured dictionary learning, enhanced detection
reliability and multi-level cooperative fusion.
The rst proposed method aims to address the noisy detections and
occlusions. In this method, sequential Monte Carlo probability hypothesis
density (SMC-PHD) ltering is the core element for accomplishing the
tracking task, where the measurements are produced by the detection based
tracking framework. To enhance the measurement model, a novel adaptive
gating strategy is developed to aid the classi cation of measurements. In
addition, online group-structured dictionary learning with a maximum voting
method is proposed to estimate robustly the target birth intensity. It
enables the new-born targets in the tracking process to be accurately initialized
from noisy sensor measurements. To improve the adaptability of the
group-structured dictionary to target appearance changes, the simultaneous
codeword optimization (SimCO) algorithm is employed for the dictionary
update.
The second proposed method relates to accurate measurement selection
of detections, which is further to re ne the noisy detections prior to the tracking
pipeline. In order to achieve more reliable measurements in the Gaussian
mixture (GM)-PHD ltering process, a global-to-local enhanced con dence
rescoring strategy is proposed by exploiting the classi cation power of a mask
region-convolutional neural network (R-CNN). Then, an improved pruning
algorithm namely soft-aggregated non-maximal suppression (Soft-ANMS) is
devised to further enhance the selection step. In addition, to avoid the misuse
of ambiguous measurements in the tracking process, person re-identi cation
(ReID) features driven by convolutional neural networks (CNNs) are integrated
to model the target appearances.
The third proposed method focuses on addressing the issues of missed
detections and occlusions. This method integrates two human detectors
with di erent characteristics (full-body and body-parts) in the GM-PHD
lter, and investigates their complementary bene ts for tracking multiple
targets. For each detector domain, a novel discriminative correlation matching
(DCM) model for integration in the feature-level fusion is proposed, and
together with spatio-temporal information is used to reduce the ambiguous
identity associations in the GM-PHD lter. Moreover, a robust fusion
center is proposed within the decision-level fusion to mitigate the sensitivity
of missed detections in the fusion process, thereby improving the fusion
performance and tracking consistency.
The e ectiveness of these proposed methods are investigated using the
MOTChallenge benchmark, which is a framework for the standardized evaluation
of multiple object tracking methods. Detailed evaluations on challenging
video datasets, as well as comparisons with recent state-of-the-art
techniques, con rm the improved multiple human tracking performance
A Computer-Aided Training (CAT) System for Short Track Speed Skating
Short track speed skating has become popular all over the world. The demands of a computer-aided training (CAT) system are booming due to this fact. However, the existing commercial systems for sports are highly dependent on expensive equipment and complicated hardware calibration.
This dissertation presents a novel CAT system for tracking multiple skaters in short track skating competitions. Aiming at the challenges, we utilize global rink information to compensate camera motion and obtain the global spatial information of skaters; apply Random Forest to fuse multiple cues and predict the blobs for each of the skaters; and finally develop a silhouette and edge-based template matching and blob growing method to allocate each blob to corresponding skaters. The proposed multiple skaters tracking algorithm organically integrates multi-cue fusion, dynamic appearance modeling, machine learning, etc. to form an efficient and robust CAT system. The effectiveness and robustness of the proposed method are presented through experiments
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition
The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.
As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications.
This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
- …