2 research outputs found
Information-Maximizing Sampling to Promote Tracking-by-Detection
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 select their samples from the vicinity of the last
predicted object location, or from its expected location using a pre-defined
motion model, which does not exploit the contents of the samples nor the
information provided by the classifier. We introduced the idea of most
informative sampling, in which the sampler attempts to select samples that
trouble the classifier of a discriminative tracker. We then proposed an active
discriminative co-tracker that embed an adversarial sampler to increase its
robustness against various tracking challenges. Experiments show that our
proposed tracker outperforms state-of-the-art trackers on various benchmark
videos.Comment: visual tracking, information-maximizing sampling, active learning,
structured sample learnin
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning
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 redundancy to address the accumulating label error
in the tracking loop, and suffer from high computational complexity as well as
tracking challenges that may interrupt all classifiers (e.g. temporal
occlusions). We propose the active co-tracking framework, in which the main
classifier of the tracker labels samples of the video sequence, and only
consults auxiliary classifier when it is uncertain. Based on the source of the
uncertainty and the differences of two classifiers (e.g. accuracy, speed,
update frequency, etc.), different policies should be taken to exchange the
information between two classifiers. Here, we introduce a reinforcement
learning approach to find the appropriate policy by considering the state of
the tracker in a specific sequence. The proposed method yields promising
results in comparison to the best tracking-by-detection approaches.Comment: Submitted to ICIP 201