15,123 research outputs found
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking
Modern visual trackers usually construct online learning models under the
assumption that the feature response has a Gaussian distribution with
target-centered peak response. Nevertheless, such an assumption is implausible
when there is progressive interference from other targets and/or background
noise, which produce sub-peaks on the tracking response map and cause model
drift. In this paper, we propose a rectified online learning approach for
sub-peak response suppression and peak response enforcement and target at
handling progressive interference in a systematic way. Our approach, referred
to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to
aggregate and align discriminative features, as well as leveraging a Boundary
Response Truncation (BRT) to reduce the variance of feature response. By fusing
with multi-scale features, SPSTracker aggregates the response distribution of
multiple sub-peaks to a single maximum peak, which enforces the discriminative
capability of features for robust object tracking. Experiments on the OTB, NFS
and VOT2018 benchmarks demonstrate that SPSTrack outperforms the
state-of-the-art real-time trackers with significant margins.Comment: Accepted as oral paper at AAAI202
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
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