6,433 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Human poses and motions are important cues for analysis of videos with people
and there is strong evidence that representations based on body pose are highly
effective for a variety of tasks such as activity recognition, content
retrieval and social signal processing. In this work, we aim to further advance
the state of the art by establishing "PoseTrack", a new large-scale benchmark
for video-based human pose estimation and articulated tracking, and bringing
together the community of researchers working on visual human analysis. The
benchmark encompasses three competition tracks focusing on i) single-frame
multi-person pose estimation, ii) multi-person pose estimation in videos, and
iii) multi-person articulated tracking. To facilitate the benchmark and
challenge we collect, annotate and release a new %large-scale benchmark dataset
that features videos with multiple people labeled with person tracks and
articulated pose. A centralized evaluation server is provided to allow
participants to evaluate on a held-out test set. We envision that the proposed
benchmark will stimulate productive research both by providing a large and
representative training dataset as well as providing a platform to objectively
evaluate and compare the proposed methods. The benchmark is freely accessible
at https://posetrack.net.Comment: www.posetrack.ne
Joint Detection and Tracking in Videos with Identification Features
Recent works have shown that combining object detection and tracking tasks,
in the case of video data, results in higher performance for both tasks, but
they require a high frame-rate as a strict requirement for performance. This is
assumption is often violated in real-world applications, when models run on
embedded devices, often at only a few frames per second.
Videos at low frame-rate suffer from large object displacements. Here
re-identification features may support to match large-displaced object
detections, but current joint detection and re-identification formulations
degrade the detector performance, as these two are contrasting tasks. In the
real-world application having separate detector and re-id models is often not
feasible, as both the memory and runtime effectively double.
Towards robust long-term tracking applicable to reduced-computational-power
devices, we propose the first joint optimization of detection, tracking and
re-identification features for videos. Notably, our joint optimization
maintains the detector performance, a typical multi-task challenge. At
inference time, we leverage detections for tracking (tracking-by-detection)
when the objects are visible, detectable and slowly moving in the image. We
leverage instead re-identification features to match objects which disappeared
(e.g. due to occlusion) for several frames or were not tracked due to fast
motion (or low-frame-rate videos). Our proposed method reaches the
state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge
among online trackers, and 3rd overall.Comment: Accepted at Image and Vision Computing Journa
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