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Integrating Social Grouping for Multitarget Tracking Across Cameras in a CRF Model
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
SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset
Multi-object tracking (MOT) has been dominated by the use of track by
detection approaches due to the success of convolutional neural networks (CNNs)
on detection in the last decade. As the datasets and bench-marking sites are
published, research direction has shifted towards yielding best accuracy on
generic scenarios including re-identification (reID) of objects while tracking.
In this study, we narrow the scope of MOT for surveillance by providing a
dedicated dataset of pedestrians and focus on in-depth analyses of well
performing multi-object trackers to observe the weak and strong sides of
state-of-the-art (SOTA) techniques for real-world applications. For this
purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with
annotated short videos captured from static cameras located on poles with 6-8
meters in height positioned for city surveillance. This provides a more focused
and specific benchmarking of MOT for outdoor surveillance compared to public
MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with
respect to the way of use of detection and reID networks on this new dataset.
The experimental results of our new dataset indicate that SOTA is still far
from high efficiency, and single-shot trackers are good candidates to unify
fast execution and accuracy with competitive performance. The dataset will be
available at: sompt22.github.ioComment: 18 pages, 3 figures, 9 tables, ECCV 202
TRECVID 2008 - goals, tasks, data, evaluation mechanisms and metrics
The TREC Video Retrieval Evaluation (TRECVID) 2008 is a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 7 years this effort has yielded a
better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. In 2008, 77 teams (see Table 1) from various research organizations --- 24 from
Asia, 39 from Europe, 13 from North America, and 1 from Australia --- participated in one or more of five tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), pre-production video (rushes) summarization, copy detection, or surveillance event detection. The copy detection and surveillance event detection tasks are being run for the first time in TRECVID.
This paper presents an overview of TRECVid in 2008
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