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Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba
Different layers in CNNs provide not only different levels of abstraction for describing the objects in the input but also encode various implicit information about them. The activ…
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba
Employing one or more additional classifiers to break the self-learning loop in tracing-by-detection has gained considerable attention. Most of such trackers merely utilize the red…
Information-Maximizing Sampling to Promote Tracking-by-Detection
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba
The performance of an adaptive tracking-by-detection algorithm not only depends on the classification and updating processes but also on the sampling. Typically, such trackers sele…
Efficient Diverse Ensemble for Discriminative Co-Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the col…
Active Collaborative Ensemble Tracking
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba +1
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 t…
Efficient Version-Space Reduction for Visual Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
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 u…