9,213 research outputs found
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. Compared to tracking with a still camera, the
images captured with a PTZ camera are highly dynamic in nature because the
camera can perform large motion resulting in quickly changing capture
conditions. Furthermore, tracking with a PTZ camera involves camera control to
position the camera on the target. For successful tracking and camera control,
the tracker must be fast enough, or has to be able to predict accurately the
next position of the target. Therefore, standard benchmarks do not allow to
assess properly the quality of a tracker for the PTZ scenario. In this work, we
use a virtual PTZ framework to evaluate different tracking algorithms and
compare their performances. We also extend the framework to add target position
prediction for the next frame, accounting for camera motion and processing
delays. By doing this, we can assess if predicting can make long-term tracking
more robust as it may help slower algorithms for keeping the target in the
field of view of the camera. Results confirm that both speed and robustness are
required for tracking under the PTZ scenario.Comment: 6 pages, 2 figures, International Conference on Pattern Recognition
and Artificial Intelligence 201
Active Collaborative Ensemble Tracking
A discriminative ensemble tracker employs multiple classifiers, each of which
casts a vote on all of the obtained samples. The votes are then aggregated in
an attempt to localize the target object. Such method relies on collective
competence and the diversity of the ensemble to approach the target/non-target
classification task from different views. However, by updating all of the
ensemble using a shared set of samples and their final labels, such diversity
is lost or reduced to the diversity provided by the underlying features or
internal classifiers' dynamics. Additionally, the classifiers do not exchange
information with each other while striving to serve the collective goal, i.e.,
better classification. In this study, we propose an active collaborative
information exchange scheme for ensemble tracking. This, not only orchestrates
different classifier towards a common goal but also provides an intelligent
update mechanism to keep the diversity of classifiers and to mitigate the
shortcomings of one with the others. The data exchange is optimized with regard
to an ensemble uncertainty utility function, and the ensemble is updated via
co-training. The evaluations demonstrate promising results realized by the
proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
Tracking-by-detection has become an attractive tracking technique, which
treats tracking as a category detection problem. However, the task in tracking
is to search for a specific object, rather than an object category as in
detection. In this paper, we propose a novel tracking framework based on
exemplar detector rather than category detector. The proposed tracker is an
ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each
detector is quite specific and discriminative, because it is trained by a
single object instance and massive negatives. To improve its adaptivity, we
update both object and background models. Experimental results on several
challenging video sequences demonstrate the effectiveness and robustness of our
tracking algorithm.Comment: ICIP201
Online learning and detection of faces with low human supervision
The final publication is available at link.springer.comWe present an efficient,online,and interactive approach for computing a classifier, called Wild Lady Ferns (WiLFs), for face learning and detection using small human supervision. More precisely, on the one hand, WiLFs combine online boosting and extremely randomized trees (Random Ferns) to compute progressively an efficient and discriminative classifier. On the other hand, WiLFs use an interactive human-machine approach that combines two complementary learning strategies to reduce considerably the degree of human supervision during learning. While the first strategy corresponds to query-by-boosting active learning, that requests human assistance over difficult samples in function of the classifier confidence, the second strategy refers to a memory-based learning which uses ¿ Exemplar-based Nearest Neighbors (¿ENN) to assist automatically the classifier. A pre-trained Convolutional Neural Network (CNN) is used to perform ¿ENN with high-level feature descriptors. The proposed approach is therefore fast (WilFs run in 1 FPS using a code not fully optimized), accurate (we obtain detection rates over 82% in complex datasets), and labor-saving (human assistance percentages of less than 20%).
As a byproduct, we demonstrate that WiLFs also perform semi-automatic annotation during learning, as while the classifier is being computed, WiLFs are discovering faces instances in input images which are used subsequently for training online the classifier. The advantages of our approach are demonstrated in synthetic and publicly available databases, showing comparable detection rates as offline approaches that require larger amounts of handmade training data.Peer ReviewedPostprint (author's final draft
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