4,592 research outputs found
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking
Identity Switching remains one of the main difficulties Multiple Object
Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches
now use sequence models to solve this problem but their training can be
affected by biases that decrease their efficiency. In this paper, we introduce
a new training procedure that confronts the algorithm to its own mistakes while
explicitly attempting to minimize the number of switches, which results in
better training. We propose an iterative scheme of building a rich training set
and using it to learn a scoring function that is an explicit proxy for the
target tracking metric. Whether using only simple geometric features or more
sophisticated ones that also take appearance into account, our approach
outperforms the state-of-the-art on several MOT benchmarks
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
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