38,012 research outputs found
Object Tracking with Multiple Instance Learning and Gaussian Mixture Model
Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes
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
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
Efficient Version-Space Reduction for Visual Tracking
Discrminative trackers, employ a classification approach to separate the
target from its background. To cope with variations of the target shape and
appearance, the classifier is updated online with different samples of the
target and the background. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.Comment: CRV'17 Conferenc
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201
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